This class continues where Statistical Machine Learning (GU4241).
It covers neural networks, graphical models, sampling algorithms, and related topics.
Prerequisites
You must have attended Statistical Computation and Introduction to Data Science (GR5206) and Statistical Machine Learning (GU4241) in order to take this class. Please make sure you meet the prerequisites; enrolled students who do not will be removed from the class.Instructors
Peter Orbanz (first half of the term)John Cunningham (second half of the term)
Office hours: After class in the class room
Teaching Assistants
Ian Kinsella ([email protected])Wenda Zhou (wz2335[email protected])
Office hours: Monday, Tuesday, 5.307.30pm. Room 1025, School of Social Works.
Slides
Slides for Part I are available here.
Slides for Part II are available here. Notebooks used to run the demonstrations are available here (usually updated before lectures).
Homework
Midterm
The midterm will be held on October 19th (section 2) and 23rd (section 1).Project
The project proposals are available here.Software
The course will be based in python. We will be using Anaconda Python 3.6. The installer may be downloaded here, and installation instructions may be found here. We will be using a custom environment file which may be found here. The material for the python tutorial may be found at the course repository.Textbooks
The course is not based on a textbook. The relevant course materials are the slides. If you would like to complement lectures and slides by further reading, these books might be useful:
Pattern Recognition and Machine Learning.
Christopher M. Bishop.
Springer, 2006.

Machine Learning: A Probabilistic Perspective.
Kevin P. Murphy.
MIT Press, 2012.
[Available online]

Deep Learning
Ian Goodfellow, Yoshua Bengio, and Aaron Courville. [Available online]
MIT Press, 2016.