CS 274A: Probabilistic Learning: Theory and Algorithms, Winter 2020

General Information

• Time: Monday and Wednesday, 2:00 to 3:20pm
• Location: ICS 180
• Instructor: Professor Padhraic Smyth: Office Hours, 4:30 to 6pm, Wednesdays, DBH 4216
• TA (50% time) Sai Prameela Konduru
• Syllabus and Schedule
• Notes: Links to notes, texts, and background reading
• Questions? please use the Piazza class Website for class-related questions and discussion, i.e., post to Piazza (either publicly to the class, or privately to the instructor or reader) rather than using email.

Homeworks

• Homeworks will be submitted and graded via Gradescope.
• Homework 1 | PDF | LaTeX | Due by noon (12pm) Thursday January 16th.
• Homework 2 | PDF | LaTeX | Due by noon (12pm) Wednesday January 29th.
• Homework 3 | PDF | LaTeX | Due by noon (12pm) Wednesday February 4th.
• Homework 4 | PDF | LaTeX | Due by noon (12pm) Monday February 24th.
• Homework 5 | PDF | LaTeX | data | Due by noon (12pm) Wednesday March 4th. Submit plots and text to Gradescope. Submit code to Canvas as a Zip file.

Prerequisites for taking this class

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

Course Goals

Students will develop a comprehensive understanding of probabilistic approaches to machine learning. 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. Your lowest scoring homework will be dropped and not included in your score. No credit for late homeworks.

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.