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: 40% (five or six assignments)
Midterm exam: 30%
Final exam: 30%

Machine Learning: A Probabilistic Perspective (by K. Murphy, 2012)
Pattern Recognition and Machine Learning (by C. Bishop, 2006, Springer)

(Tentative schedule: https://docs.google.com/spreadsheets/d/1hyLHHOfrCPbpAXmP0PT6z1V5xQ7DvIHlFsf_4cy3sUg/edit?usp=sharing)
Week 1: Introduction, Review on Probability Theory
Week 2: Density estimation
Week 3: Linear regression
Week 4: Logistic regression
Week 5: SVM
Week 6: SVM & kernel
Week 7: Neural networks
Week 8: Midterm
Week 9: Graphical model
Week 10: Belief propagation
Week 11: Clustering
Week 12: PCA
Week 13: VAE
Week 14: GAN
Week 15: Reinforcement learning
Week 16: Final exam

Assignment: every two or three weeks
Exam: midterm and final (closed book)

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