Spring 2012: Machine Learning

General Information

Time: Wednesdays 12:00-3:00 PM Place: CBIM 22
Instructor: Tina Eliassi-Rad Office hours: Wednesdays 5:30-6:30 PM in CBIM 08
TA: Chetan Tonde TA office hours: Tuesdays 1:00-3:00 PM in Hill 402
Course number: 16:198:536 Credits: 3


This graduate-level course introduces the theory, algorithms, and applications of machine learning. Topics covered include supervised learning, unsupervised learning, semi-supervised learning, learning theory, and reinforcement learning.

Prerequisites: Calculus and linear algebra. An introductory course on statistics and probability. Algorithms and programming (MATLAB).




Notes, Policies, and Guidelines

Schedule / Syllabus (Subject to Change)





Jan 18


Decision Trees

Naive Bayes

Logistic Regression

Generative vs. Discriminative Classifiers

Bishop 1.3, 1.4, 1.6, 14.4, Mitchell 1, 3, plus follow links in the previous cell. Optional: Discriminative vs. Generative Models, Guestrin's NB.


Jan 25

Linear Regression


Bias-Variance Tradeoff



Bishop 3, 4, Optional: HTF 3, 4.


Feb 1

Learning Theory

PAC Learning

VC Dimension

Mitchell 7, HTF 7, COLT survey, Generalization Bounds, Schapire's Theoretical ML.  Optional: Online Learning.

Guest Lecturer:
Chris Mesterharm

Feb 8

Kernel Methods

Support Vector Machines
(SVM-1, SVM-2)

Bishop 6.1, 6.2, 6.3, 7.1 plus follow links in the previous cell.  Optional: Bishop 6.4, HTF 6, 12.

HW#1 Out

Feb 15

Algorithms to Affect Influence Propagation on Large Graphs

Heterogeneity Meets Rarity: Mining Multi-Faceted Diamond

Follow links in the previous cell.

Guest Speakers: Hanghang Tong

& Jingrui He

Feb 22


Neural Networks

Bishop 4.1.7, 5.1, 5.2, 5.3, 5.5.  Optional: Mitchell 4, HTF 11.

HW #1 Due

Feb 29

Graphical Models

Bishop 8, Bayesian Networks.  Optional: Mitchell 6, CRF, HMM, Ghahramani's HMM & BN, Bishop 13.1, 13.2, 11.


Mar 7

Ensemble Methods
(Boosting, Random Forests)

In-class Project Pitches

Bishop 14.1, 14.2, 14.3 plus follow links in the previous cells.  Optional: HTF: 10, 15, 16

HW #2 Out

Project Proposals Due

Mar 14

No Class -- Spring Break



Mar 21


Expectation Maximization

Mixture of Gaussians

Bishop 9.1, 9.2, 9.3, 9.4.  Optional: HTF 14; x-means, k-means++


Mar 28

Dimensionality Reduction

Bishop 12 plus follow links in the previous cell.  Optional: M.E. Wall, et al.'s PCA

HW #2 Due

Apr 4

In-class Exam

Active Learning

Semi-Supervised Learning

Follow links in the previous cell. Optional: Co-training.

HW #3 Out

Apr 11

Building Accurate and Comprehensible Classification Models 

What can 20,000 models teach us?

Follow links in the previous cell.

Guest Speakers:
David Martens 


Apr 18

Reinforcement Learning (1), (2)

Follow links in the previous cell. Optional: Mitchell 13.

Guest Lecturer: Michael Littman

HW #3 Due

Apr 25

In-class Project Presentations



May 2

No Class


Project Reports Due

Other Topics of Interest