Machine learning is the science of collecting and analyzing data and turning it into predictions, encapsulated knowledge, or actions. There are many ways and scenarios by which data can be obtained, many different models and algorithms for data analysis, and many potential applications. In recent years machine learning has attracted attention due to commercial successes and widespread use.
The course gives a broad introduction to machine learning aimed
at upper level undergraduates and beginning graduate students.
Some mathematical aptitude is required, but generally speaking we focus on
baseline algorithms, practical aspects, and breadth and leave more sophisticated aspects as well as detailed analysis to more advanced courses:
Statistical Pattern Recognition
(Fall 2016),
Machine Learning Seminar
(Fall 2016),
Computational Learning Theory
(Fall 2015),
Learning, Planning and Acting in Complex Environments
(Fall 2014)
Syllabus:
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.
Prerequisites:
Comp 15 and COMP/MATH 22 or 61 or consent of instructor. Comp 160 is highly recommended. You will also need a minimal amount of calculus.