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: 30% (five or six assignments)
Midterm exam: 30%
Final exam: 30%
Pop-up quiz + attendance: 10% (no exception unless excuses are given in advance)

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

https://docs.google.com/spreadsheets/d/1hyLHHOfrCPbpAXmP0PT6z1V5xQ7DvIHlFsf_4cy3sUg/edit?usp=sharing

Lecture: 15:30-16:45 Tuesday/Thursday
Assignment: every two weeks (approx.)
Exam: midterm and final (closed book)

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