CS 274A: Probabilistic Learning: Theory and Algorithms, Winter 2017

General Information

Homeworks

Prerequisites for taking this class
Knowledge of basic concepts in probability, multivariate calculus, and linear algebra are required for this course. Please note that a good understanding of probability in particular is important for this class.

Course Goals
Students will develop a comprehensive understanding of probabilistic approaches to learning from data. Probabilistic learning is a key component in many areas within modern computer science, including artificial intelligence, data mining, speech recognition, computer vision, bioinformatics, and so forth. The course will provide a tutorial introduction to the basic principles of probabilistic modeling and then demonstrate the application of these principles to the analysis, development, and practical use of machine learning algorithms. Topics covered will include probabilistic modeling, defining likelihoods, parameter estimation using likelihood and Bayesian techniques, probabilistic approaches to classification, clustering, and regression, and related topics such as model selection and bias/variance tradeoffs.

Grading Policy
Final grades will be based on a combination of homework assignments and exams: 30% homeworks, 30% midterm, and 40% final. No credit for late homeworks - instead your lowest scoring homework will be dropped and not included in your score.

Academic Integrity
Students are expected to be read and be familiar with the Academic Integrity Policy for this class. Failure to adhere to this policy can result in a student receiving a failing grade in the class.