Machine learning is the study of algorithms and statistical methods that computers use to "learn" patterns and inferences in order to perform a specific task without explicit instruction. This course mainly aims at providing mathematical/statistical methods, which are essential in machine learning. A wide range of topics will be covered, including but not limited to density estimation, latent variable models, mixture models, clustering, classification, dimensionality reduction, regression, support vector machines, kernel methods, multi-layer perceptrons, and deep learning.

A basic understanding of probability/statistics and linear algebra
Python programming language

Assignments: 45%
Midterm exam1: 15%
Midterm exam2: 15%
Final exam: 15%
Class Participation: 10%

Machine Learning: A Probabilistic Perspective (by K. Murphy, 2012)
Pattern Recognition and Machine Learning (by C. Bishop, Springer, 2006)
Deep Learning (C. Bishop and H. Bishop, Springer, 2024)
Reinforcement Learning (by R. Sutton and A. Barto, MIT Press, 2020)

Tentative schedule: https://docs.google.com/spreadsheets/d/e/2PACX-1vRd2A8bm0avoKXjcekoCtg7jxSTh-tk06fMr0_GcsDDKYLHdYKkp8S_YkyZYuKYgJbq_KQx-oe-I5KR/pubhtml?gid=0&single=true

Assignment: about four assignments
Exam: midterms and final (closed book)

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