




Time:

Monday and Wednesday from 12:001:20pm (WEH 7500) (Recitations start on Jan 15)

Recitations:

Thursday 7:008:00pm (Doherty 2315)

Piazza Webpage:

https://piazza.com/class/i4mskrc82yo1pi

Course Description:

Machine Learning is concerned with computer programs that
automatically improve their performance through experience (e.g.,
programs that learn to recognize human faces, recommend music and
movies, and drive autonomous robots). This course covers the
theory and practical algorithms for machine learning from a
variety of perspectives. We cover topics such as Bayesian
networks, decision tree learning, Support Vector Machines,
statistical learning methods, unsupervised learning and
reinforcement learning. The course covers theoretical concepts
such as inductive bias, the PAC learning framework, Bayesian
learning methods, marginbased learning, and Occam's Razor. Short
programming assignments include handson experiments with various
learning algorithms. This course is
designed to give a graduatelevel student a thorough grounding in
the methodologies, technologies, mathematics and algorithms
currently needed by people who do research in machine learning.

Prerequisites:

Students entering the class are
expected to have a preexisting working knowledge of probability,
linear algebra, statistics and algorithms, though the class has been
designed to allow students with a strong numerate background to catch
up and fully participate. In addition, recitation sessions will be held
to review some basic concepts.

Textbook:

 Machine Learning, Tom Mitchell. (optional)
 Pattern Recognition and Machine Learning, Christopher Bishop. (optional)
 The Elements of Statistical Learning: Data Mining, Inference and
Prediction, Trevor Hastie, Robert Tibshirani, Jerome Friedman. (optional)
 Machine Learning: A Probabilistic Perspective, Kevin P. Murphy. (optional)

Grading:

 Midterm # 1(25%)
 Homeworks (30%)
 Final project (20%)
 Midterm # 2 (25%)

Auditing:

At this stage, unfortunately we cannot allow audits. We do not have enough space for registered students so clearly we cannot accommodate any audits.


