# 6.892 Machine learning seminar

####
Prof. Tommi Jaakkola

tommi@ai.mit.edu (preferred
point of contact)

NE43-735, x3-0440

Prereq.: permission of instructor

Meets: M/W 2.30-4pm, 26-322.

### Description:

Statistical machine learning concerns with automated,
formalized methods with the ability to adapt, infer or learn from
experience for the purpose of prediction and decision making. The aim of
this class is to provide the students with fundamentals of various
machine learning techniques so that they can readily apply, analyze, or
adjust existing methods. The emphasis will be on representational and
computational issues. A wide range of topics will be covered such as
representation of probabilities with graphs, inference and estimation on
graphs, approximate methods, model selection, clustering,
generalization.

### Background and other classes:

The students are expected to know the material in 6.893 taught by Prof
Viola (basics of estimation theory, linear algebra). 6.432 or a similar
class would provide an excellent background for this class. Previous
exposure to graph theory, information theory, or statistical physics
would be helpful but not required. The class is complementary to 9.641,
now taught by Prof Seung, but similar to the earlier 9.641 taught by
Prof Jordan (differs, however, in the emphasis and partly in the choice
of the topics). Some of the topics e.g. support vector machines are
amply covered in 9.520 and such material will either be excluded or
emphasized differently in this class.

### Format and requirements:

The format for the class is a mixture of lectures and paper
presentations by the attendees. There will be weekly assignments in the
form of brief critiques, proofs, analyses, or projects. Tutorials can be
arranged in several key topics, as needed.

### Topics:

Elements of graphical models

Density estimation, classification, clustering
Graph representations and their properties
Exact inference algorithms
Approximate inference (sampling, graphical, variational)
Decision analysis
Gaussian process models and support vector machines
Kernel based methods and graphical models
Clustering
Model selection
Model averaging (Bayesian, bagging, boosting)
Dynamic models (temporally extended decision problems)
Misc topics covered include information geometry, group theory
(invariances), and select applications