Computer Science B555
|Class Days:||Tu, Th|
|Algebra Required:||Basics: vector spaces, matrices, independence, solving linear systems.|
|Calculus Required:||Basics: discrete and continuous functions, differentiation, integration.|
|Days Per Week Offered:||Two.|
|Keywords:||Machine Learning, statistical inference, classification, regression, distribution learning.|
|Description:||The course objective is to study the theory and practice of constructing algorithms that learn (functions) and choose optimal decisions from data and experience. Machine learning is a field with goals overlapping with other disciplines, in particular, statistics, algorithms, engineering, or optimization theory. It also has wide applications to a number of scientific areas such as finance, life sciences, social sciences, or medicine. The class will cover theoretical foundations of machine learning but also provide examples from classification, regression, and statistical distribution learning. This is a core Computer Science course.
The course covers about 75% of the following topics, depending on the year:
Pattern Recognition and Machine Learning - by C. M. Bishop, Springer 2006.
Machine Learning - by Tom M. Mitchell, McGraw-Hill, 1997.
The Elements of Statistical Learning - by T. Hastie, R. Tibshirani, and J. Friedman, 2009.
|Formal Computing Lab:||No|
|How Software is Used:||To provide illustrative demos.|
|Problem Sets:||Four homework assignments and five thought questions.|
|Data Analysis:||Basic implementation and analysis of methods taught in class.|
|Presentation:||Traditional white-board; power point when needed; demos.|
|Exams:||Final exam (final week).|
|Comments:||Instructor's code and demos are in MATLAB. Homework assignments will contain programming, but students are not required to use MATLAB.|