Machine Learning (original) (raw)

Date

Content

Readings

Notes

Jan 18

Introduction

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

Regularization

Bias-Variance Tradeoff

Overfitting

Cross-Validation

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

Perceptron

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

K-means

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
(PCA, ICA, CCA)

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

Alexandru Niculescu-Mizil

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