An optional preparatory course for students with no prior programming experience and limited experience in college-level STEM classes. Basics of programming including variables, control flow, subroutines, and problem solving in a hands-on, collaborative environment. The class will meet over the latter half of the semester, and prepare students to enter CS11 with prior exposure to topics that CS 11 discusses in depth. Pass/fail grading.

This course will prepare undergraduates to function effectively and efficiently as undergraduate teaching assistants. Through this course, students will learn pedagogical techniques that match learner needs; discuss ethical and social concerns that UTAs face in the course of a semester; and problem solve together issues that arise as teaching assistants. This course is designed in a learner centered model requiring your active and engaged participation. Through your willingness to share your experiences and expertise and your collaboration with your fellow UTA we will together construct meaningful solutions to difficulty situations. Faculty from Computer Science will participate in some of the sessions as co-facilitators. Students will be expected to complete short readings; keep a reflective blog of your learning as a teacher; give a short final presentation on a topic of interest that you want to explore in more depth to help you in your TA class.

The Humanities is increasingly making use of computerized methods in gathering and interpreting research data. Simultaneously, the world has seen a dramatic increase in the dissemination of numerical claims-—the products of data science--in forms ranging from infographics in newspapers to interactive visualizations on social media and other web sites. This course covers selected topics in computing approaches for the Humanities. The aim is to equip Humanities students with basic awareness and skills in using digital research methods and applying that knowledge to understanding and interrogating the numerical claims produced by data scientists. Additionally, the course aims to provide experience with a variety of computational tools to approach Humanities research 9 questions, topics, and datasets. All examples and the applications of the computational tools will be drawn from the Humanities. We hope to empower Humanities students to understand and interpret research conducted with computerized methods. After this course, they should be familiar with the steps involved in working with data. They will be prepared to ask questions of numerical claims they encounter in publications and online. Where did the source data come from? How was it processed? Why was it visualized in this way and how might the visualization help or hinder appropriate interpretation? They will also be prepared to engage in research together with data scientists and thus form better teams. Non-Humanities students will be better equipped to engage with Humanists by getting a sense for the needs of the field. They will also gain awareness of the different topics explored in the Humanities (such as language, history, prosopography, geolocalization, etc). Students will develop an interest in these disciplines and add them to a well-rounded curriculum. Further, students will benefit from exposure to and hands on experience with real world applications of data science techniques in the Humanities. The format of the course will be modeled after introductory Computer Science (CS) courses at Tufts. The course is open to all Tufts undergraduates and will be counted as a Humanities credit.

Computers are indispensable tools for research. This does not only hold for more technical fields such as physics or chemistry but also for the Humanities and the Social Sciences. While most students are competent users of standard software such as word processing or spreadsheets, the real power of the computer is unleashed when we are able to program it ourselves and make it do exactly what we want it to do.

This course is aimed at people who want to learn how to use computer science to solve basic information processing problems, such as analyzing text data and performing elementary statistics on them. It will cover elementary principles of computer science and will teach the student to independently write their own programs in the computer language Python.

This course is meant for people who have little or no previous experience in computer science. Therefore, in this course we do not assume that the students already know how to write computer programs. However, computer programming is a skill, and learning a new skill takes substantial amounts of effort and time. So the fact that this course is aimed at beginners does not mean that it is easy, or that it will involve less work than our other introduction courses, like e.g. COMP 11. On the contrary, it is very likely the case that this course will involves more effort than other introductory programming courses, if only because the fact that we do not assume any previous experience means that the road to our goal is going to be longer.

IMPORTANT NOTE: Passing this course does NOT fulfill the A&S Mathematics distribution requirement.

The study of computer science centers on two complementary aspects of the discipline. First, computer science is fundamentally concerned with the problem-solving methodologies it derives from its foundational fields: the design principles of engineering, mathematical theory, and scientific empirical study. Second, these methodologies are applied in the complex context of a modern day computing system. In this course we will address both of these important aspects. As a means for developing your design skills, we will discuss the fundamental features of a high level, general purpose programming language -- namely C++-- and learn how to use it as a tool for problem solving. We will also consider the performance of solutions, and how to apply both analytical and empirical assessment techniques. Finally, we will explore the Unix operating system as a context for problem solving. (Additional 2 hr weekly lab time scheduled at first class meeting.) Recommendations: High school algebra. No prior programming experience is necessary.

The study of computer science centers on two complementary aspects of the discipline. First, computer science is fundamentally concerned with the problem-solving methodologies it derives from its foundational fields: the design principles of engineering, mathematical theory, and scientific empirical study. Second, these methodologies are applied in the complex context of a modern day computing system. In this course we will address both of these important aspects. As a means for developing your design skills, we will discuss the fundamental features of a high level, general purpose programming language -- namely C++-- and learn how to use it as a tool for problem solving. We will also consider the performance of solutions, and how to apply both analytical and empirical assessment techniques. Finally, we will explore the Unix operating system as a context for problem solving. (Additional 2 hr weekly lab time scheduled at first class meeting.) Recommendations: High school algebra. No prior programming experience is necessary.

The study of computer science centers on two complementary aspects of the discipline. First, computer science is fundamentally concerned with the problem-solving methodologies it derives from its foundational fields: the design principles of engineering, mathematical theory, and scientific empirical study. Second, these methodologies are applied in the complex context of a modern day computing system. In this course we will address both of these important aspects. As a means for developing your design skills, we will discuss the fundamental features of a high level, general purpose programming language -- namely C++-- and learn how to use it as a tool for problem solving. We will also consider the performance of solutions, and how to apply both analytical and empirical assessment techniques. Finally, we will explore the Unix operating system as a context for problem solving. (Additional 2 hr weekly lab time scheduled at first class meeting.) Recommendations: High school algebra. No prior programming experience is necessary.

The study of computer science centers on two complementary aspects of the discipline. First, computer science is fundamentally concerned with the problem-solving methodologies it derives from its foundational fields: the design principles of engineering, mathematical theory, and scientific empirical study. Second, these methodologies are applied in the complex context of a modern day computing system. In this course we will address both of these important aspects. As a means for developing your design skills, we will discuss the fundamental features of a high level, general purpose programming language -- namely C++-- and learn how to use it as a tool for problem solving. We will also consider the performance of solutions, and how to apply both analytical and empirical assessment techniques. Finally, we will explore the Unix operating system as a context for problem solving. (Additional 2 hr weekly lab time scheduled at first class meeting.) Recommendations: High school algebra. No prior programming experience is necessary.

The study of computer science centers on two complementary aspects of the discipline. First, computer science is fundamentally concerned with the problem-solving methodologies it derives from its foundational fields: the design principles of engineering, mathematical theory, and scientific empirical study. Second, these methodologies are applied in the complex context of a modern day computing system. In this course we will address both of these important aspects. As a means for developing your design skills, we will discuss the fundamental features of a high level, general purpose programming language -- namely C++-- and learn how to use it as a tool for problem solving. We will also consider the performance of solutions, and how to apply both analytical and empirical assessment techniques. Finally, we will explore the Unix operating system as a context for problem solving. (Additional 2 hr weekly lab time scheduled at first class meeting.) Recommendations: High school algebra. No prior programming experience is necessary.

Relevance of computer technologies to policy development. Internet architecture and basic networking, the Web, cloud architectures, cryptography, security and privacy, AI and machine learning, and open-source systems. Developing technologies, including quantum computing and post-quantum cryptography.

How computing systems work: bits, bytes, the representation of information, the CPU, assembly language, programming languages. Networking: including peering, packets, and the Internet. Algorithms and the fundamental limitations of computing.

Weekly, peer-led workshops exploring topics in computer science. Emphasis on the collaborative and problem-solving nature of computer science. No prior programming experience is necessary. Students must apply to enroll – see departmental website for details.

A second course in computer science. Data structures and algorithms are studied through major programming projects in the C++ programming language. Topics include linked lists, trees, graphs, dynamic storage allocation, and recursion.

A second course in computer science. Data structures and algorithms are studied through major programming projects in the C++ programming language. Topics include linked lists, trees, graphs, dynamic storage allocation, and recursion.

A second course in computer science. Data structures and algorithms are studied through major programming projects in the C++ programming language. Topics include linked lists, trees, graphs, dynamic storage allocation, and recursion.

A second course in computer science. Data structures and algorithms are studied through major programming projects in the C++ programming language. Topics include linked lists, trees, graphs, dynamic storage allocation, and recursion.

A second course in computer science. Data structures and algorithms are studied through major programming projects in the C++ programming language. Topics include linked lists, trees, graphs, dynamic storage allocation, and recursion.

An introduction to techniques, principles, and practices of writing computer programs for the World Wide Web. Server and browser capabilities and limits. Media types, handlers, and limitations. Web programming languages and techniques. Web security, privacy, and commerce. Lectures augmented with programming projects illustrating concepts and current practice.

An introduction to techniques, principles, and practices of writing computer programs for the World Wide Web. Server and browser capabilities and limits. Media types, handlers, and limitations. Web programming languages and techniques. Web security, privacy, and commerce. Lectures augmented with programming projects illustrating concepts and current practice.

Principles, design, and development of games. Game structure, engineering, physics, testing, 2D and 3D rendering, user interfaces, sound, and animation. Security of online games. Applications of Economics, Music, and Psychology in crafting games. Projects include writing game design documents, developing an interactive fiction game, and building a functional game in a team.

Fundamentals of programming for data-intensive science. Data structures and algorithms for data manipulation, cleaning, and preparation. Design of data manipulation programs. Coding standards and practices. Use and creation of software libraries. Techniques for improving program performance. Examples drawn from data preparation and transformation, statistical data analysis, machine learning, deep learning, and deep data science including recommendation systems and trend analysis.

Fundamentals of programming for data-intensive science. Data structures and algorithms for data manipulation, cleaning, and preparation. Design of data manipulation programs. Coding standards and practices. Use and creation of software libraries. Techniques for improving program performance. Examples drawn from data preparation and transformation, statistical data analysis, machine learning, deep learning, and deep data science including recommendation systems and trend analysis.

In COMP 40, you will learn about both high-level programming design principles and the low-level structure of computing machines. Design strategies will focus on modularity, abstraction, and separation of interface from implementation. The following topics on machine structure are covered: memory, caches, registers, machine arithmetic, and bitwise operations. We will also investigate the structure of assembly code, relocatable object code, binary machine code, and the translations between them. You will gain a deep understanding of all of these concepts via large-scale, realistic programming projects.

Mandatory lab will be held Fridays: sign up in SIS.

See https://tufts.qualtrics.com/jfe/form/SV_bxtFLggljQPFwsm for the form required to get approval to enroll in this class.

Computer organization including performance measurement, instruction set architectures, digital arithmetic, processor datapath, control, pipelining, memory hierarchy, caches and input/output.

(Cross-listed as Mathematics 61.) Sets, relations and functions, logic and methods of proof, combinatorics, graphs and digraphs.

(Cross-listed as Mathematics 61.) Sets, relations and functions, logic and methods of proof, combinatorics, graphs and digraphs.

(Cross-listed as Mathematics 61.) Sets, relations and functions, logic and methods of proof, combinatorics, graphs and digraphs.

(Cross-listed as Mathematics 61.) Sets, relations and functions, logic and methods of proof, combinatorics, graphs and digraphs.

(Cross-listed as Mathematics 61.) Sets, relations and functions, logic and methods of proof, combinatorics, graphs and digraphs.

Object-oriented programming (OOP) and design, using the Java language. General OOP concepts (classes and instances, methods, inheritance) plus specifics of programming in Java, with emphasis on application to graphical user interfaces (GUIs). Design and programming projects using Java and toolkits.

Requirements analysis and design of a senior capstone project. Requirements analysis and elicitation methods, and prototyping. Design principles and methods, including designing for usability, security, testability, performance, and scaling. Project management and planning, including cost and effort estimation. Writing effective documentation.

Principles and application of computer programming languages. Emphasizes ideas and techniques most relevant to practitioners, but includes foundations crucial for intellectual rigor: abstract syntax, lambda calculus, type systems, dynamic semantics. Case studies, reinforced by programming exercises. Grounding sufficient to read professional literature.

Principles and application of computer programming languages. Emphasizes ideas and techniques most relevant to practitioners, but includes foundations crucial for intellectual rigor: abstract syntax, lambda calculus, type systems, dynamic semantics. Case studies, reinforced by programming exercises. Grounding sufficient to read professional literature.

Principles and application of computer programming languages. Emphasizes ideas and techniques most relevant to practitioners, but includes foundations crucial for intellectual rigor: abstract syntax, lambda calculus, type systems, dynamic semantics. Case studies, reinforced by programming exercises. Grounding sufficient to read professional literature.

Design and implementation of computer communication networks, protocols, and applications, with an emphasis on the Internet protocol suite. Network architectures and programming interfaces. Data link, transport, and routing protocols. Congestion sources and remedies. Addressing and naming in local area and wide area networks. Network security and network management.

Design and implementation of computer communication networks, protocols, and applications, with an emphasis on the Internet protocol suite. Network architectures and programming interfaces. Data link, transport, and routing protocols. Congestion sources and remedies. Addressing and naming in local area and wide area networks. Network security and network management.

Design and implementation of computer communication networks, protocols, and applications, with an emphasis on the Internet protocol suite. Network architectures and programming interfaces. Data link, transport, and routing protocols. Congestion sources and remedies. Addressing and naming in local area and wide area networks. Network security and network management.

Fundamental concepts of database management systems. Topics include: data models (relational, object-oriented, and others); the SQL query language; implementation techniques of database management systems (storage and index structures, concurrency control, recovery, and query processing); management of unstructured and semistructured data; and scientific data collections.

Fundamental concepts of database management systems. Topics include: data models (relational, object-oriented, and others); the SQL query language; implementation techniques of database management systems (storage and index structures, concurrency control, recovery, and query processing); management of unstructured and semistructured data; and scientific data collections.

Fundamental concepts of database management systems. Topics include: data models (relational, object-oriented, and others); the SQL query language; implementation techniques of database management systems (storage and index structures, concurrency control, recovery, and query processing); management of unstructured and semistructured data; and scientific data collections.

Fundamental concepts of database management systems. Topics include: data models (relational, object-oriented, and others); the SQL query language; implementation techniques of database management systems (storage and index structures, concurrency control, recovery, and query processing); management of unstructured and semistructured data; and scientific data collections.

A systems perspective on host-based and network-based computer security. Current vulnerabilities and measures for protecting hosts and networks. Firewalls and intrusion detection systems. Principles illustrated through hands-on programming projects.

A systems perspective on host-based and network-based computer security. Current vulnerabilities and measures for protecting hosts and networks. Firewalls and intrusion detection systems. Principles illustrated through hands-on programming projects.

A systems perspective on host-based and network-based computer security. Current vulnerabilities and measures for protecting hosts and networks. Firewalls and intrusion detection systems. Principles illustrated through hands-on programming projects.

A systems perspective on host-based and network-based computer security. Current vulnerabilities and measures for protecting hosts and networks. Firewalls and intrusion detection systems. Principles illustrated through hands-on programming projects.

Please note that this course was formerly numbered COMP 150-IDS. The World Wide Web, one of the most important developments of our time, is a unique and in many ways innovative distributed system. This course will explore the design decisions that enabled the Web's success, and from those will derive important and sometimes surprising principles for the design of other modern distributed systems. We will introduce and draw comparisons with more traditional distributed system designs, including distributed objects, client/server, pub/sub, reliable queuing, etc. We will also study a few (easily understood) research papers and some of the core specifications of the Web. Specific topics to be covered include: global uniform naming; location-independence; layering and leaky abstractions; end-to-end arguments and decentralized innovation; Metcalfe's law and network effects; extensibility and evolution of distributed systems; declarative vs. procedural languages; Postel's law; caching; and HTML/XML/JSON document-based computing vs. RPC. The purpose of this course is not to teach Web site development, but rather to explore lessons in system design that can be applied to any complex software system. More detailed course information can be found at https://www.cs.tufts.edu/comp/117/shouldItakeit

Cloud computing fundamentals, including cloud architecture, scalability, elasticity, and metrics of cloud performance including service-level objectives (SLOs) and service-level agreements (SLAs). Cloud programming models and abstractions including Map/Reduce. Persistent storage mechanisms, including key/value stores and cold storage. Geo-distributed cloud systems. Cloud networking, including data center architecture, software defined networking, and middleboxes. Cloud security.

Cloud computing fundamentals, including cloud architecture, scalability, elasticity, and metrics of cloud performance including service-level objectives (SLOs) and service-level agreements (SLAs). Cloud programming models and abstractions including Map/Reduce. Persistent storage mechanisms, including key/value stores and cold storage. Geo-distributed cloud systems. Cloud networking, including data center architecture, software defined networking, and middleboxes. Cloud security.

Cloud computing fundamentals, including cloud architecture, scalability, elasticity, and metrics of cloud performance including service-level objectives (SLOs) and service-level agreements (SLAs). Cloud programming models and abstractions including Map/Reduce. Persistent storage mechanisms, including key/value stores and cold storage. Geo-distributed cloud systems. Cloud networking, including data center architecture, software defined networking, and middleboxes. Cloud security.

"Big Data" deals with techniques for collecting, processing, analyzing and acting on data at internet scale: unprecedented speed, scale, and complexity.

This course introduces the latest techniques and infrastructures developed for big data including parallel and distributed database systems, map-reduce infrastructures, scalable platforms for complex data types, stream processing systems, and cloud-based computing. The course content will be a blend of theory, algorithms and practical (hands on) work.

Prerequisites: A beginning course in databases, some familiarity with Python, shell programming, Java, Scala, SQL and JavaScript.

"Big Data" deals with techniques for collecting, processing, analyzing and acting on data at internet scale: unprecedented speed, scale, and complexity.

This course introduces the latest techniques and infrastructures developed for big data including parallel and distributed database systems, map-reduce infrastructures, scalable platforms for complex data types, stream processing systems, and cloud-based computing. The course content will be a blend of theory, algorithms and practical (hands on) work.

Prerequisites: A beginning course in databases, some familiarity with Python, shell programming, Java, Scala, SQL and JavaScript.

"Big Data" deals with techniques for collecting, processing, analyzing and acting on data at internet scale: unprecedented speed, scale, and complexity.

This course introduces the latest techniques and infrastructures developed for big data including parallel and distributed database systems, map-reduce infrastructures, scalable platforms for complex data types, stream processing systems, and cloud-based computing. The course content will be a blend of theory, algorithms and practical (hands on) work.

Prerequisites: A beginning course in databases, some familiarity with Python, shell programming, Java, Scala, SQL and JavaScript.

Web applications are complex systems that deliver a plethora of functionality to a large number of users, and also exhibit unique behaviors and demands in terms of performance, scalability, usability, and security. Web engineering is an emerging and multidisciplinary process that is used to create quality web applications. This course will discuss the limits of current web technologies, the similarities and differences between web and software engineering, design, information and service architectures, content management, and testing disciplines. Frameworks such as Rails, Spring, and Symfony will be emphasized and used. Projects will involve search, cloud computing, location-based services, and mobile web development.

Software engineering is an engineered discipline in which the aim is the production of software products, delivered on time and within a set budget, that satisfies the client’s needs. It covers all aspects of software production ranging from the early stage of product concept to design and implementation to post-delivery maintenance. This course covers the major concepts and techniques of software engineering including understanding system requirements, finding appropriate engineering compromises, effective methods of design, coding, and testing, team software development, and the application of engineering tools so that students can prepare for their future careers as software engineers. The course will combine a strong technical focus with a project providing the opportunity to obtain hands-on experiences on entire phases and workflow of the software process.

Software engineering is an engineered discipline in which the aim is the production of software products, delivered on time and within a set budget, that satisfies the client’s needs. It covers all aspects of software production ranging from the early stage of product concept to design and implementation to post-delivery maintenance. This course covers the major concepts and techniques of software engineering including understanding system requirements, finding appropriate engineering compromises, effective methods of design, coding, and testing, team software development, and the application of engineering tools so that students can prepare for their future careers as software engineers. The course will combine a strong technical focus with a project providing the opportunity to obtain hands-on experiences on entire phases and workflow of the software process.

Software engineering is an engineered discipline in which the aim is the production of software products, delivered on time and within a set budget, that satisfies the client’s needs. It covers all aspects of software production ranging from the early stage of product concept to design and implementation to post-delivery maintenance. This course covers the major concepts and techniques of software engineering including understanding system requirements, finding appropriate engineering compromises, effective methods of design, coding, and testing, team software development, and the application of engineering tools so that students can prepare for their future careers as software engineers. The course will combine a strong technical focus with a project providing the opportunity to obtain hands-on experiences on entire phases and workflow of the software process.

Software engineering is an engineered discipline in which the aim is the production of software products, delivered on time and within a set budget, that satisfies the client’s needs. It covers all aspects of software production ranging from the early stage of product concept to design and implementation to post-delivery maintenance. This course covers the major concepts and techniques of software engineering including understanding system requirements, finding appropriate engineering compromises, effective methods of design, coding, and testing, team software development, and the application of engineering tools so that students can prepare for their future careers as software engineers. The course will combine a strong technical focus with a project providing the opportunity to obtain hands-on experiences on entire phases and workflow of the software process.

History, theory, and computational methods of artificial intelligence. Basic concepts include representation of knowledge and computational methods for reasoning. One or two application areas will be studied, to be selected from expert systems, robotics, computer vision, natural language understanding, and planning.

History, theory, and computational methods of artificial intelligence. Basic concepts include representation of knowledge and computational methods for reasoning. One or two application areas will be studied, to be selected from expert systems, robotics, computer vision, natural language understanding, and planning.

History, theory, and computational methods of artificial intelligence. Basic concepts include representation of knowledge and computational methods for reasoning. One or two application areas will be studied, to be selected from expert systems, robotics, computer vision, natural language understanding, and planning.

History, theory, and computational methods of artificial intelligence. Basic concepts include representation of knowledge and computational methods for reasoning. One or two application areas will be studied, to be selected from expert systems, robotics, computer vision, natural language understanding, and planning.

This course will provide an overview of the up and coming field of human-robot interaction (HRI) which is located squarely in the intersection of psychology, human factors engineering, computer science, and robotics. HRI has become a major research focus recently with the NSF's National Robotics Initiative and the push countries around the globe to develop robots for various societal tasks, from new flexible and adaptive robots for industrial manufacturing, to socially assistive robots for eldercare. In this course, we will examine this field from an interdisciplinary perspective, reading key papers in HRI that intersect computer science, robotics, cognitive and social psychology (since there is no suitable textbook yet, all reading materials will be made available). Students will give short presentations on HRI studies and designs and work in interdisciplinary groups on a term project which will require them to design and conduct an HRI study.

This course will provide an overview of the up and coming field of human-robot interaction (HRI) which is located squarely in the intersection of psychology, human factors engineering, computer science, and robotics. HRI has become a major research focus recently with the NSF's National Robotics Initiative and the push countries around the globe to develop robots for various societal tasks, from new flexible and adaptive robots for industrial manufacturing, to socially assistive robots for eldercare. In this course, we will examine this field from an interdisciplinary perspective, reading key papers in HRI that intersect computer science, robotics, cognitive and social psychology (since there is no suitable textbook yet, all reading materials will be made available). Students will give short presentations on HRI studies and designs and work in interdisciplinary groups on a term project which will require them to design and conduct an HRI study.

This course will provide an overview of the up and coming field of human-robot interaction (HRI) which is located squarely in the intersection of psychology, human factors engineering, computer science, and robotics. HRI has become a major research focus recently with the NSF's National Robotics Initiative and the push countries around the globe to develop robots for various societal tasks, from new flexible and adaptive robots for industrial manufacturing, to socially assistive robots for eldercare. In this course, we will examine this field from an interdisciplinary perspective, reading key papers in HRI that intersect computer science, robotics, cognitive and social psychology (since there is no suitable textbook yet, all reading materials will be made available). Students will give short presentations on HRI studies and designs and work in interdisciplinary groups on a term project which will require them to design and conduct an HRI study.

Scientific logic of using computational models for testing theories in cognitive science. Connectionist and Bayesian models; agent-based simulation. Emphasis upon using models in combination with empirical data to test theories. Appropriate use and critical evaluation of computational modeling as found in scientific publications. Recommendations: COMP 10, 11, or some programming experience.

Scientific logic of using computational models for testing theories in cognitive science. Connectionist and Bayesian models; agent-based simulation. Emphasis upon using models in combination with empirical data to test theories. Appropriate use and critical evaluation of computational modeling as found in scientific publications. Recommendations: COMP 10, 11, or some programming experience.

Scientific logic of using computational models for testing theories in cognitive science. Connectionist and Bayesian models; agent-based simulation. Emphasis upon using models in combination with empirical data to test theories. Appropriate use and critical evaluation of computational modeling as found in scientific publications. Recommendations: COMP 10, 11, or some programming experience.

An overview of methods whereby computers can learn from data or experience and make decisions accordingly. Topics include supervised learning, unsupervised learning, reinforcement learning, and knowledge extraction from large databases with applications to science, engineering, and medicine.

An overview of methods whereby computers can learn from data or experience and make decisions accordingly. Topics include supervised learning, unsupervised learning, reinforcement learning, and knowledge extraction from large databases with applications to science, engineering, and medicine.

An overview of methods whereby computers can learn from data or experience and make decisions accordingly. Topics include supervised learning, unsupervised learning, reinforcement learning, and knowledge extraction from large databases with applications to science, engineering, and medicine.

An overview of methods whereby computers can learn from data or experience and make decisions accordingly. Topics include supervised learning, unsupervised learning, reinforcement learning, and knowledge extraction from large databases with applications to science, engineering, and medicine.

Deep neural networks and their applications including computer vision and natural language processing. Feed-forward, convolutional, and recurrent neural networks. Techniques for training deep neural networks, including optimization, regularization, and usage of related software.

Deep neural networks and their applications including computer vision and natural language processing. Feed-forward, convolutional, and recurrent neural networks. Techniques for training deep neural networks, including optimization, regularization, and usage of related software.

Deep neural networks and their applications including computer vision and natural language processing. Feed-forward, convolutional, and recurrent neural networks. Techniques for training deep neural networks, including optimization, regularization, and usage of related software.

"Reinforcement learning problems involve learning what to do — how to map situations to actions — so as to maximize a numerical reward signal." - Sutton and Barto ("Reinforcement Learning: An Introduction", course textbook)

This course will focus on agents that much learn, plan, and act in complex, non-deterministic environments. We will cover the main theory and approaches of Reinforcement Learning (RL), along with common software libraries and packages used to implement and test RL algorithms. The course is a graduate seminar with assigned readings and discussions. The content of the course will be guided in part by the interests of the students. It will cover at least the first several chapters of the course textbook. Beyond that, we will move to more advanced and recent readings from the field (e.g., transfer learning and deep RL) with an aim towards focusing on the practical successes and challenges relating to reinforcement learning.

There will be a programming component to the course in the form of a few short assignments and a final projects.

Approved as a category 2 elective in Data Science (analysis and interfaces).

"Reinforcement learning problems involve learning what to do — how to map situations to actions — so as to maximize a numerical reward signal." - Sutton and Barto ("Reinforcement Learning: An Introduction", course textbook)

This course will focus on agents that much learn, plan, and act in complex, non-deterministic environments. We will cover the main theory and approaches of Reinforcement Learning (RL), along with common software libraries and packages used to implement and test RL algorithms. The course is a graduate seminar with assigned readings and discussions. The content of the course will be guided in part by the interests of the students. It will cover at least the first several chapters of the course textbook. Beyond that, we will move to more advanced and recent readings from the field (e.g., transfer learning and deep RL) with an aim towards focusing on the practical successes and challenges relating to reinforcement learning.

There will be a programming component to the course in the form of a few short assignments and a final projects.

Approved as a category 2 elective in Data Science (analysis and interfaces).

"Reinforcement learning problems involve learning what to do — how to map situations to actions — so as to maximize a numerical reward signal." - Sutton and Barto ("Reinforcement Learning: An Introduction", course textbook)

This course will focus on agents that much learn, plan, and act in complex, non-deterministic environments. We will cover the main theory and approaches of Reinforcement Learning (RL), along with common software libraries and packages used to implement and test RL algorithms. The course is a graduate seminar with assigned readings and discussions. The content of the course will be guided in part by the interests of the students. It will cover at least the first several chapters of the course textbook. Beyond that, we will move to more advanced and recent readings from the field (e.g., transfer learning and deep RL) with an aim towards focusing on the practical successes and challenges relating to reinforcement learning.

There will be a programming component to the course in the form of a few short assignments and a final projects.

Approved as a category 2 elective in Data Science (analysis and interfaces).

"Reinforcement learning problems involve learning what to do — how to map situations to actions — so as to maximize a numerical reward signal." - Sutton and Barto ("Reinforcement Learning: An Introduction", course textbook)

This course will focus on agents that much learn, plan, and act in complex, non-deterministic environments. We will cover the main theory and approaches of Reinforcement Learning (RL), along with common software libraries and packages used to implement and test RL algorithms. The course is a graduate seminar with assigned readings and discussions. The content of the course will be guided in part by the interests of the students. It will cover at least the first several chapters of the course textbook. Beyond that, we will move to more advanced and recent readings from the field (e.g., transfer learning and deep RL) with an aim towards focusing on the practical successes and challenges relating to reinforcement learning.

There will be a programming component to the course in the form of a few short assignments and a final projects.

Approved as a category 2 elective in Data Science (analysis and interfaces).

This course will provide an overview of the ethical problems and challenges prompted by current and future technological advances in AI, robotics, and human-robot interaction. It will start by reviewing the philosophical foundations of the main ethical theories (virtue ethics, deontology, utilitarianism) and link them to different algorithmic approaches in artificial agents (rule-based, utility-based, behavior-based, etc.). Explicating and contrasting the assumptions underlying each algorithmic approach (e.g., policy-based decision-making vs. rule-based reasoning), functional tradeoffs and implications for autonomous robots and AI systems will be discussed. The scope will then be widened to moral psychology and human-robot/human-technology interaction to move beyond individual autonomous systems into the realm of social interactions between humans and autonomous systems, discussing the societal implications of AI and robot technology. Social, economical, legal, and military ramifications will be considered, with the aim of exposing the unique challenges AI and robot technology pose for humanity, compared to other disruptive technologies, but also they unique opportunities these technologies enable for current and future generations.

Please note: for Spring 2022, the course will meet in person on Tuesdays from 6:00 - 6:50pm. In addition to this meeting time, there will also be virtual, asynchronous lectures.

This course will provide an overview of the ethical problems and challenges prompted by current and future technological advances in AI, robotics, and human-robot interaction. It will start by reviewing the philosophical foundations of the main ethical theories (virtue ethics, deontology, utilitarianism) and link them to different algorithmic approaches in artificial agents (rule-based, utility-based, behavior-based, etc.). Explicating and contrasting the assumptions underlying each algorithmic approach (e.g., policy-based decision-making vs. rule-based reasoning), functional tradeoffs and implications for autonomous robots and AI systems will be discussed. The scope will then be widened to moral psychology and human-robot/human-technology interaction to move beyond individual autonomous systems into the realm of social interactions between humans and autonomous systems, discussing the societal implications of AI and robot technology. Social, economical, legal, and military ramifications will be considered, with the aim of exposing the unique challenges AI and robot technology pose for humanity, compared to other disruptive technologies, but also they unique opportunities these technologies enable for current and future generations.

Please note: for Spring 2022, the course will meet in person on Tuesdays from 6:00 - 6:50pm. In addition to this meeting time, there will also be virtual, asynchronous lectures.

Mathematical foundations of the study of graphs and networks that arise as social, biological and Internet networks. Random graph models, community structure and inference problems, network dynamics, cascading. Example networks drawn from the application domains will be case studies as a companion to the general mathematical theory.

Approved as a category 2 elective in Data Science (analysis and interfaces).

Mathematical foundations of the study of graphs and networks that arise as social, biological and Internet networks. Random graph models, community structure and inference problems, network dynamics, cascading. Example networks drawn from the application domains will be case studies as a companion to the general mathematical theory.

Approved as a category 2 elective in Data Science (analysis and interfaces).

Mathematical foundations of the study of graphs and networks that arise as social, biological and Internet networks. Random graph models, community structure and inference problems, network dynamics, cascading. Example networks drawn from the application domains will be case studies as a companion to the general mathematical theory.

Approved as a category 2 elective in Data Science (analysis and interfaces).

(Cross-listed w/ EE 126.) This course teaches advanced concepts of modern computer architecture, starting from the basic 5-stage pipelines and progressing to out-of-order superscalar processors. This course introduces the techniques used to maximize single-thread performance within the constraints of memory technology, power consumption, and the inherent instruction-level parallelism of applications. Students will also gain hands on hardware experience by implementing a 5-stage MIPS processor in VHDL. Recommendations: EE 14 or COMP 40; and ES4.

This course is an introduction to the complexity theoretic foundations of quantum computing and centers around the question: “what classes of computational problems are (not) solvable on computational models with quantum mechanical information processing resources?”. We start off with a gentle introduction to quantum mechanics, quantum computing, models of computation and computational complexity. We then proceed to defining quantum complexity classes and their relationship to the complexity zoo. We then move to special topics such as Hamiltonian complexity, noisy intermediate-scale quantum complexity theory, quantum interactive proof systems, bosonic quantum complexity classes, the interface between physics and theoretical computer science, and more, depending on interest. The goal is to bring students to the research frontier.

This course is an introduction to the complexity theoretic foundations of quantum computing and centers around the question: “what classes of computational problems are (not) solvable on computational models with quantum mechanical information processing resources?”. We start off with a gentle introduction to quantum mechanics, quantum computing, models of computation and computational complexity. We then proceed to defining quantum complexity classes and their relationship to the complexity zoo. We then move to special topics such as Hamiltonian complexity, noisy intermediate-scale quantum complexity theory, quantum interactive proof systems, bosonic quantum complexity classes, the interface between physics and theoretical computer science, and more, depending on interest. The goal is to bring students to the research frontier.

This course is an introduction to the complexity theoretic foundations of quantum computing and centers around the question: “what classes of computational problems are (not) solvable on computational models with quantum mechanical information processing resources?”. We start off with a gentle introduction to quantum mechanics, quantum computing, models of computation and computational complexity. We then proceed to defining quantum complexity classes and their relationship to the complexity zoo. We then move to special topics such as Hamiltonian complexity, noisy intermediate-scale quantum complexity theory, quantum interactive proof systems, bosonic quantum complexity classes, the interface between physics and theoretical computer science, and more, depending on interest. The goal is to bring students to the research frontier.

What does it mean to do data science in a society filled with competing economic, social, and political interests? In this course, we will draw from historical perspectives on present-day issues in computer science to develop a social and political understanding of what the problem is, how it came to be, and what we can do about it. Our topic of focus will be surveillance, a problem that intersects economics through capitalism, race through histories of the policing of Black and Brown people, and power through systems of control. Throughout the three arcs comprising the class – (1) defining surveillance and learning its history, (2) developing an ethical inquiry and critique of surveillance technology, and (3) applying this practice to present day related problems – we will build “muscles” of analysis and practice that will build a foundation for budding data scientists to navigate a social and political world.

This course introduces the basics of contemporary mobile application development using Apple's iOS platform. The main requirement of the course is to build a functioning application in iOS. The course is divided into six modules, each of which covers a different aspect of development which is used in a final project. Module 1 begins with the major features of the Swift programming language and its standard library, along with use of the Xcode IDE for Swift development. Basic language features are covered lightly so that extensive discussion may be focused on differentiating features of the language including closures, optionals, the Swift type system (tuple/enum/struct/class/func), and generics. Module 2 focuses on elements of Functional Reactive Programming with Apple’s Combine library. Module 3 deals with correct application architecture, using a uni-directional dataflow model. Module 4 covers drawing, touch handling, layout and programming for devices of various sizes and aspect ratios, making extensive use of Apple's SwiftUI technology. Module 5 takes the student through a full animation cycle showing which elements of the UI can be smoothly animated and how. Module 6 finishes instruction with a discussion of navigation, tabular data presentation, and application state management. Frequent small assignments progress from basic programming to realistic app development with a focus on responsive device graphics and algorithms. Code design and architecture are emphasized. The course culminates in a final project which integrates all topics discussed into a single coherent whole.

Organizations today collect and analyze massive amounts of information for important decision-making applications. However, these large-scale analytics often compromise sensitive user information, such as medical or financial records. In this course, we will survey and apply state of the art techniques in privacy-preserving data science, such as differential privacy and secure multi-party computation, to learn how to build systems that provide useful results while still respecting user privacy. Students will be expected to read research papers, give in-class presentations, and complete a final project utilizing real-world data.

The emerging research area of Bayesian Deep Learning seeks to combine the benefits of modern deep learning (scalable gradient-based training of flexible neural networks for regression and classification) with the benefits of modern Bayesian statistical methods to estimate probabilities and make decisions under uncertainty. The goal of this course is to bring students to the forefront of knowledge in this area through coding exercises, student-led discussion of recent literature, and an in-depth project. Covered topics include key modeling innovations (e.g. function approximation and deep generative models), learning paradigms (e.g. variational inference), and implementation using modern automatic differentiation frameworks. By completing a 2-month self-designed research project, students will gain experience with designing, implementing, and evaluating new contributions in this exciting research space.

The emerging research area of Bayesian Deep Learning seeks to combine the benefits of modern deep learning (scalable gradient-based training of flexible neural networks for regression and classification) with the benefits of modern Bayesian statistical methods to estimate probabilities and make decisions under uncertainty. The goal of this course is to bring students to the forefront of knowledge in this area through coding exercises, student-led discussion of recent literature, and an in-depth project. Covered topics include key modeling innovations (e.g. function approximation and deep generative models), learning paradigms (e.g. variational inference), and implementation using modern automatic differentiation frameworks. By completing a 2-month self-designed research project, students will gain experience with designing, implementing, and evaluating new contributions in this exciting research space.

The emerging research area of Bayesian Deep Learning seeks to combine the benefits of modern deep learning (scalable gradient-based training of flexible neural networks for regression and classification) with the benefits of modern Bayesian statistical methods to estimate probabilities and make decisions under uncertainty. The goal of this course is to bring students to the forefront of knowledge in this area through coding exercises, student-led discussion of recent literature, and an in-depth project. Covered topics include key modeling innovations (e.g. function approximation and deep generative models), learning paradigms (e.g. variational inference), and implementation using modern automatic differentiation frameworks. By completing a 2-month self-designed research project, students will gain experience with designing, implementing, and evaluating new contributions in this exciting research space.

Students will learn to estimate probabilities of events through simulation; perform exploratory probabilistic data analysis to identify outliers, artifacts, and patterns; formulate and assess hypotheses about heterogeneous high dimensional data; appropriately visualize data to assess quality and support interpretation; present statistical data analyses clearly and contextually to specific audiences. These aims will be achieved through homework assignments, in class exercises and discussions, readings, and a course project. There will be weekly assignments throughout the term, alternating between problem sets in R and structured parts of the project.

Students will learn to estimate probabilities of events through simulation; perform exploratory probabilistic data analysis to identify outliers, artifacts, and patterns; formulate and assess hypotheses about heterogeneous high dimensional data; appropriately visualize data to assess quality and support interpretation; present statistical data analyses clearly and contextually to specific audiences. These aims will be achieved through homework assignments, in class exercises and discussions, readings, and a course project. There will be weekly assignments throughout the term, alternating between problem sets in R and structured parts of the project.

Students will learn to estimate probabilities of events through simulation; perform exploratory probabilistic data analysis to identify outliers, artifacts, and patterns; formulate and assess hypotheses about heterogeneous high dimensional data; appropriately visualize data to assess quality and support interpretation; present statistical data analyses clearly and contextually to specific audiences. These aims will be achieved through homework assignments, in class exercises and discussions, readings, and a course project. There will be weekly assignments throughout the term, alternating between problem sets in R and structured parts of the project.

Introduction to the study of algorithms. Strategies such as divide-and-conquer, greedy methods, and dynamic programming. Graph algorithms, sorting, searching, integer arithmetic, hashing, and NP-complete problems.

Introduction to the study of algorithms. Strategies such as divide-and-conquer, greedy methods, and dynamic programming. Graph algorithms, sorting, searching, integer arithmetic, hashing, and NP-complete problems.

Introduction to the study of algorithms. Strategies such as divide-and-conquer, greedy methods, and dynamic programming. Graph algorithms, sorting, searching, integer arithmetic, hashing, and NP-complete problems.

Introduction to the study of algorithms. Strategies such as divide-and-conquer, greedy methods, and dynamic programming. Graph algorithms, sorting, searching, integer arithmetic, hashing, and NP-complete problems.

(Cross-listed as MATH 181.)Design and analysis of algorithms for geometric problems. Topics include proof of lower bounds, convex hulls, searching and point location, plane sweep and arrangements of lines, Voronoi diagrams, intersection problems, decomposition and partitioning, farthest-pairs and closest-pairs, rectilinear computational geometry.

Models of computation: Turing machines, pushdown automata, and finite automata. Grammars and formal languages including context-free languages and regular sets. Important problems including the halting problem and language equivalence theorems.

Models of computation: Turing machines, pushdown automata, and finite automata. Grammars and formal languages including context-free languages and regular sets. Important problems including the halting problem and language equivalence theorems.

Models of computation: Turing machines, pushdown automata, and finite automata. Grammars and formal languages including context-free languages and regular sets. Important problems including the halting problem and language equivalence theorems.

Models of computation: Turing machines, pushdown automata, and finite automata. Grammars and formal languages including context-free languages and regular sets. Important problems including the halting problem and language equivalence theorems.

This course explores the fundamentals of computer graphics, including representing digital images, 2D rasterization and anti-aliasing, 3D rendering via ray casting, ray tracing and radiosity, viewing transformations, 3D shape representation, and an introduction to modeling and computer animation. Assignments and projects require a good working knowledge of the C programming language.

This course explores the fundamentals of computer graphics, including representing digital images, 2D rasterization and anti-aliasing, 3D rendering via ray casting, ray tracing and radiosity, viewing transformations, 3D shape representation, and an introduction to modeling and computer animation. Assignments and projects require a good working knowledge of the C programming language.

This course explores the fundamentals of computer graphics, including representing digital images, 2D rasterization and anti-aliasing, 3D rendering via ray casting, ray tracing and radiosity, viewing transformations, 3D shape representation, and an introduction to modeling and computer animation. Assignments and projects require a good working knowledge of the C programming language.

There is a myth that the Internet erases borders. But as Internet companies' ability to place localized ads show, that's false. What's more accurate is that the Internet complicates a nation's ability to control of the flow of information within its borders. (This is not a new challenge for sovereign nations; consider the telegraph.) This fluidity has created great economic opportunity and simplified trans-border access, the latter potentially threatening security and other basic state functions. With bits increasingly controlling the world around us, the Digital Revolution poses a highly disruptive threat. In this course, well explore cyber clashes in the civilian sector: from jurisdictional issues and the challenges posed by new technologies to criminal activities and impacts on civil infrastructures. While several of the topics are also covered in International Cyber Conflict: An Introduction to Power and Conflict in Cyberspace, DHP P249, the intersection between the two courses will be relatively minimal. Cyber in the Civilian Sector will have a greater focus on technology and, naturally enough, on the civilian, as opposed to national-security, side of the house.

This course is an introduction to the legal issues of cyberspace. Legal issues in this domain are complex. Technology has been evolving faster than the law's ability to handle the changes, so partially this course will be an education in the legislating and policy making of moving targets. It will also be an education in jurisdiction, privacy, surveillance, and copyright as it relates to the Internet. The perspective will be from US law and jurisprudence, although there will be periodic forays into international issues. Topics covered will include cyberlaw and cybergovernance, the Digital Revolution and its impact on First, Fourth, and Fifth Amendment issues, copyright in the Digital Age, and the Computer Fraud and Abuse Act.

This course is an introduction to the legal issues of cyberspace. Legal issues in this domain are complex. Technology has been evolving faster than the law's ability to handle the changes, so partially this course will be an education in the legislating and policy making of moving targets. It will also be an education in jurisdiction, privacy, surveillance, and copyright as it relates to the Internet. The perspective will be from US law and jurisprudence, although there will be periodic forays into international issues. Topics covered will include cyberlaw and cybergovernance, the Digital Revolution and its impact on First, Fourth, and Fifth Amendment issues, copyright in the Digital Age, and the Computer Fraud and Abuse Act.

Relevance of computer technologies to policy development. Internet architecture and basic networking, the Web, cloud architectures, cryptography, security and privacy, AI and machine learning, and open-source systems. Developing technologies, including quantum computing and post-quantum cryptography. A recitation and graduate-level assignments are required. Four credit hours. Prerequisites: Graduate standing in a discipline other than Computer Science, Data Science, Bioinformatics, Cognitive Science, or Human-Robot Interaction.

Graduate version of COMP13. How computing systems work: bits, bytes, the representation of information, the CPU, assembly language, programming languages. Networking: including peering, packets, and the Internet. Algorithms and the fundamental limitations of computing. Prerequisites: Graduate standing in a discipline other than Computer Science, Data Science, Bioinformatics, Cognitive Science, or Human-Robot Interaction.

Algorithms for data manipulation, cleaning, and preparation. Design of data manipulation programs. Coding standards and practices. Use and creation of software libraries. Techniques for improving program performance. Examples drawn from data preparation and transformation, statistical data analysis, machine learning, deep learning, and deep data science including recommendation systems and trend analysis. Co-located with CS 30. Students enrolled in CS 204 are expected to meet requirements for CS 30, along with an additional term project.

Application of data science and analytic principles to the solution of a real-world problem in a group setting. Requirements analysis, review of available data sources, and proposal of a solution strategy to the problem.

Crosslisted as ME 193. This course explores emerging topics in data science and statistical learning with applications to the three pillars of sustainability (environmental, economic, and social). Students learn to build, estimate, and interpret models that describe phenomena in the broad area of energy and environmental decision-making with an emphasis on social justice. Students leave the course as both critical consumers and responsible producers of data-driven analysis. The objectives of this class include i) learning a suite of data-driven modeling and prediction tools, ii) building the programming and computing expertise to use those tools, and iii) developing the ability to formulate an analysis to answer sustainability questions of interest to industry and/or government partners.

What does it mean to do data science in a society filled with competing economic, social, and political interests? In this course, we will draw from historical perspectives on present-day issues in computer science to develop a social and political understanding of what the problem is, how it came to be, and what we can do about it. Our topic of focus will be surveillance, a problem that intersects economics through capitalism, race through histories of the policing of Black and Brown people, and power through systems of control. Throughout the three arcs comprising the class – (1) defining surveillance and learning its history, (2) developing an ethical inquiry and critique of surveillance technology, and (3) applying this practice to present day related problems – we will build “muscles” of analysis and practice that will build a foundation for budding data scientists to navigate a social and political world.

Students will learn to estimate probabilities of events through simulation; perform exploratory probabilistic data analysis to identify outliers, artifacts, and patterns; formulate and assess hypotheses about heterogeneous high dimensional data; appropriately visualize data to assess quality and support interpretation; present statistical data analyses clearly and contextually to specific audiences. These aims will be achieved through homework assignments, in class exercises and discussions, readings, and a course project. There will be weekly assignments throughout the term, alternating between problem sets in R and structured parts of the project.

While it might be obvious why we need research in biology or history, Computer Science research is necessary to reinvent the field and to drive discoveries across many disciplines. This course will teach you the foundations of research. Students will work with a faculty mentor and a student group on a research project. Research topics include machine learning, computer security, quantum computing, human-robotics interaction, computational biology, computational geometry, and others. The course will cover topics including identifying and formulating research problems, reading and evaluating research papers, literature searching, self-guided learning, designing research studies, and data analysis. Students will practice working in a team, goal setting, activity logging, and communicating with others. This is a non-coding class. No prior coding or CS experience is required. Student groups are expected to develop a research proposal by the end of the semester and to be well-prepared to participate in future Computer Science research experiences.

Introduction to robot construction, programming, computer vision, event-based programming, artificial intelligence, and elementary controls. Basic principles of robotics for students with minimal or no prior programming/building background. In-class competition-based laboratories and hands-on group projects using the LEGO MINDSTORMS platform.