Course Schedule and Notes


ï‚·  No class on September 3 due to Labor Day.

ï‚·  No class on November 21 due to Thanksgiving.

Two important notes:
  1. The lecture schedule below is tentative and subject to change. We will move at a pace dictated by class discussions.
  2. The slides from Mitchell's book are a good summary of that book but usually not of my lectures. 

Topics

Tentative Dates (Click to watch video) Livestream

Reading
and Slides from Mitchell

Reading from Murphy 

Notes & Other Material

Lecture Recordings from Fall 2017

Course goals, philosophy, teaching style, policies and mechanics

8/27M

 

 

 

8/28/17 

Introduction: The Machine Learning Process

8/29W

CH 1
slides

CH 1

Lecture Notes

8/28/17, 8/30/17 

Concept learning, inductive bias

9/3M, 9/5W, 9/7F, 9/10M

CH 2 (excluding 2.5.3, 2.6.3)
slides

CH 3.1, 3.2

9/6/17, 9/11/17, 9/13/17 

Information Theory

9/12W, 9/17M, 9/19W, 9/21F, 9/24M



CH 2.8

Roni's Tutorial.

visual information theory, Recitation Notes

9/18/17, 9/20/17, 9/25/17 

Decision trees, overfitting, Occam's razor

9/24M, 9/26W, 10/1M

CH 3
slides

CH 16.2

9/27/17, 10/2/17 

Review of Prob & Stats. Linear Regression

10/1M, 10/3W, 10/8M, 10/10W, 10/12F

 

CH 2.2, 2.5, CH 7

Lecture Notes

10/4/17, 10/9/17, 10/11/17 

Mid-term exam

Monday 10/15 at 6:30pm - 9:30pm LOCATION: DH 2315 and McConomy

 

 

Example mid-term exam.

Important tip: Try to solve this exam in writing before you look at the solutions.

 

Neural Networks; Deep Learning

10/10W, 10/15M, 10/17W, 10/22M, 10/24W, 10/29M, 11/2F

CH 4
slides

CH 16.5, CH 28

Very clear overview of Deep Learning, Zack's Deep Learning Slides

10/16/17, 10/18/17, 10/23/17, 10/25/17, 10/30/17 

Bayesian learning, MAP and ML 

10/31W, 11/5M, 11/7W, 11/9F

CH 6 slides

CH 5.1--5.4

11/1/17, 11/6/17 

Naive Bayes

11/9F

CH 6

CH 3.5

11/8/17 

Undirected Graphical Models (MRFs), Hidden Markov Models, Directed Graphical Models (Bayes Nets)

11/12M, 11/14W, 11/16F

CH 6

CH 10, CH 19, CH 5.3.2.4, CH 17, Online Intro

Wayne Ward's slides

11/13/17, 11/15/17, 11/27/17, 11/29/17 

Reinforcement Learning

11/19M, 11/21W, 11/26M

 

 

EM Algorithm

11/28W

CH 6

CH 11

Mixture of Gaussians Example

12/4/17 

Instance based (non-parametric) learning, KNN

11/30F

CH 8 slides

CH 14.1--14.3

12/6/17 

Kernel Based methods, Maximum-Margin Classifiers, misc.

12/3M

CH 14.4, CH 14.5

12/6/17, 12/8/17 

Recitation/Review

12/5W

 

s15 review session by head TA

 

FINAL EXAM

December 13th at 1:00pm-4:00pm