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.

A hands-on introductory course in bioinformatics for students with little or no computer science background. Basic programming skills for data manipulation and analysis.Methods and applications of online tools for sequence alignment, molecular phylogeny, gene expression data analysis, and linking molecular variation to disease.

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.

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.

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.

Structure and function of the main components of computer systems: processors, main memory, and disk storage devices. Processor design, including instruction set design and interpretation. Assembly language programming. Implementation issues for high-level languages. Mandatory lab will be held Fridays: sign up in SIS.

When we learn to program, we specify problem solutions as a single sequence of computations in a fixed, determined order. But the world isn’t like that. Deer run into the woods, people talk on their phones, it rains. Nothing forces these things to happen one at a time, in a fixed order. They happen concurrently. We want to write concurrent programs, because we want to model the real world, because our computer systems actually have concurrent activities, and also to improve the performance or usability of our programs. The ubiquity of distributed applications and modern, multicore processors makes concurrent programming an essential skill. This course explores different models of concurrent programming: students will gain competence in conventional shared-memory threads programming, and at least one natively concurrent programming model (actors or CSP). Time permitting, we may look at other models. We’ll look at classic problems (like deadlock) and synchronization mechanisms (semaphores, locks, barriers). Students will complete a substantial team programming project using these tools and techniques, and they will present their work to the class.

(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.

(Crosslisted as EE 128). Fundamental issues in operating system design. Concurrent processes: synchronization, sharing, deadlock, scheduling. Relevant hardware properties of uniprocessor and multiprocessor computer systems.

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.

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 http://www.cs.tufts.edu/comp/150IDS/shouldItakeit

"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.

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.

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.

(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.

Information theory as a systematic framework to address fundamental laws and limits of data compression and digital communication. Source coding/data compression; information measures on discrete memory-less sources; practical schemes and algorithms for lossless data compression such as Huffman coding, arithmetic coding, Lempel-Ziv Coding; channel coding for reliable communication and rate distortion for lossy source compression. Advanced topics such as information theoretic cryptography.

Graph and network data are ubiquitous and often in large scale. Graph data are generally characterized by the graph structure and data attached to graph nodes or edges. Machine learning is an important approach to automated information extraction from graph data. As most learning models (e.g. neural networks) only accept vectors as the input, graph data need special model designs. Existing models generally fall into two categories: 1) models that learn vector representations of graph data, and 2) models that take graphs as the input. In this course, we will start with a shallow introduction of graph theory and then discuss a series of learning methods for graphs. The topics include graph embedding, generative models, graph neural networks, kernel methods, and a few applications. The course work consists of 2 to 3 projects and a final project.

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

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.

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

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

The course will cover a series of case studies on the use of computing to solve important problems in the developing regions, such as healthcare, education, and governance. The course will involve a semester long hands-on project which will focus on one of the above challenges.

"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).

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 as CLS 191

This course introduces students to methods for working with corpora and more generalized collections of text. The course builds particularly upon services available in the Natural Language Toolkit, but considers as well other workflows (such as https://weblicht.sfs.uni-tuebingen.de/, https://nlp.stanford.edu/software/, https://gate.ac.uk/, etc.). We will consider how NLP can address the two dimensions of scale: working with more materials than humans can read and working with materials in more languages than human beings can learn. The challenge is not simply to work with large bodies in a handful of languages such as English, Chinese, Spanish and Arabic but in the 24 official languages of the EU, the 22 languages with official standing in India, and historical languages of the human cultural record. “Language wrangling” involves the application of all available methods to push beyond translations, whether produced by machines or humans, and to explore the language directly. Linguists have done this for centuries by adding rich annotation to individual texts. NLP and crowd-sourcing allow us to scale these methods up to large collections.

Who this course is for:

- Computer Science students who want to familiarize themselves with the methods and open questions associated with corpora.
- Students from the Humanities and Social sciences who wish to analyze textual sources. While this course will serve students interested in Premodern studies, it can also be of particular interest to students in International Relations who wish to develop research projects working with current sources in various languages as well

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

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.

Introduction to graph theory, including trees, matchings, colorings, planar graphs. This class does not require programming, but there are applications in computational geometry, computational biology, and algorithms. Students may select either an implementation or a theoretical final project.

Creating a new programming language requires designers to draw on a wide range of skills: theory to ensure their creation has a well-defined semantics, engineering to ensure they produce an efficient implementation, and aesthetics to ensure the whole is coherent and pleasing to programmers. In this class, we will study the principles and tools that underlie programming language design. We will consider both general-purpose languages, which are languages intended for almost any programming task, and domain-specific languages, which are languages focused on specific tasks. The class will be a combination of lectures and seminar-style discussions about selected academic papers. Students will use the knowledge they acquire to design and implement their own domain-specific programming language.

Cloud computing, which is the practice of renting software and hardware services from providers who run large-scale data centers, has become critical to modern society. We rely on software running within cloud data centers when shopping (e.g., at Amazon), when conducting financial transactions (e.g., at an online broker), when collaborating at work (e.g., using Google Docs), and even when playing games (e.g., Fortnite). Failures or performance problems within these data centers or the software running on them can have widespread effects and be devastating.

In this course, we will examine failures in cloud environments and discuss important research on tools that use systems knowledge, machine learning, and statistics to help engineers diagnose them. To provide students with necessary background, we will start with a brief introduction to cloud computing and the software systems that make cloud computing possible. The course will involve reading research papers, homework assignments, and coding-based projects. It is recommended for graduate students and advanced upper-level undergraduates.

This course will provide an introduction to the state-of-the-art in socially intelligent assistive robotics. This exciting new area of research combines assistive robotics, in which robots provide help or assistance to human users, especially users with disabilities, with social robotics, in which robots interact with people in socially appropriate ways. A major area of focus for the course will be socially assistive robotics, in which robots provide assistance with health, wellness, and education-related goals through hands-off social interaction alone, but the course will also touch on recent work on incorporating socially intelligent behavior into more traditional assistive robotic technologies such as wheelchair-mounted arms and mobile manipulators. Students completing this course will become familiar with the technical, experimental, and ethical challenges of research in this area through readings from robotics, computer science, and disability studies. Students will be expected to read and discuss papers, give short presentations, and complete an individual or small group project in which they develop a research or design project from conception to final evaluation. Cross listed as ME-0149-SIR

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.

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.

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.

Fundamentals of python programming for data analysis. Common python data structures and algorithms. Design of python programs. Coding standards and practices. Use and creation of software libraries. Examples drawn from data preparation and transformation, statistical data analysis, machine learning, deep learning, and deep data science including recommendation systems and trend analysis. Labs utilizing iPython and the Jupyter data analysis workflow framework.

This course is used to fulfill the doctoral student community/residence requirement. This requirement may be satisfied over any four semesters and concurrently with the other requirements. Comp 250 may be taken up to four times for credit. (1 SHU)

TBD

Can we be inspired by caterpillars to design useful robots? Can we use computers to deduce the neuro-mechanical commands that make a caterpillar move forward? What do caterpillars and soft-tissue robots have in common? This class brings such interdisciplinary research experiences to first-year Engineering students through the lens of computational design. We learn in this course about computational modeling, the meaning of computational design and how it can help engineers in making best design decisions. The course will use MATLAB as a computational platform, and cover fundamentals such as a solution space, design decision variables, constraints, optimal points within the space and searching the design space using efficient algorithms, exhaustive enumeration, and approximate algorithms.

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.