Date Lecture Topics Readings and useful links Handouts

Jan 11 Intro to ML

Decision Trees

Slides



video Machine learning examples

Well defined machine learning problem

Decision tree learning Mitchell: Ch 3

Bishop: Ch 14.4

The Discipline of Machine Learning

Jan 13 Decision Tree learning



Review of Probability



Annotated slides



video The big picture

Overfitting Random variables, probabilities Andrew Moore's Basic Probability Tutorial

Bishop: Ch. 1 thru 1.2.3

Bishop: Ch 2 thru 2.2 HW1 out Jan 14

Jan 18



Annotated slides



video Probability and Estimation Bayes rule

MLE

MAP Andrew Moore's Basic Probability Tutorial

Bishop: Ch. 1 thru 1.2.3

Bishop: Ch 2 thru 2.2

Jan 25

Slides

Annotated Slides



video Gaussian Naive Bayes Gaussian Bayes classifiers

Document classification

Brain image classification

Form of decision surfaces Mitchell: Naive Bayes and Logistic Regression HW1 due

HW2 out

Feb 1



Slides

Annotated slides



video Linear Regression Generative/Discriminative models

minimizing squared error and maximizing data likelihood

bias-variance decomposition

regularization

Feb 3 Practical Issues



Feature selection

Overfitting

Bias-Variance tradeoff

Feb 8

Annotated slides



video Graphical models 1 Bayes nets

representing joint distributions with conditional independence assumptions Bishop: Ch 8, through 8.2 HW3 out

Feb 15

slides

video Graphical models 2 D-separation and Conditional Independence

Inference

Learning from fully observed data

Learning from partially observed data

Feb 22

annotated slides

video

Graphical models 4 Mixture of Gaussians clustering

Learning Bayes Net structure - Chow Liu Intro. to Graphical Models, K. Murphy

Graphical Models tutorial, M. Jordan

HW3 due

HW4 out

Feb 24

Learning

Theory

annotated slides

video ComputationalLearningTheory PAC Learning Mitchell: Ch. 7

Mar 3 Midterm Exam in class

open notes, open book, no internet Midterm

Solution

Mar 15

Learning

Theory

annotated slides

video ComputationalLearningTheory Mistake bounds

Weighted Majority Algorithm Mitchell: Ch. 7

Mar 22

annotated slides



Hidden Markov Models Markov models

HMM's and Bayes Nets

Other probabilistic time series models Bishop Ch. 13

Mar 24

slides



video Neural Networks Non-linear regression

Backpropagation and Gradient descent

Learning hidden layer representations Mitchell Ch. 4

Bishop Ch. 5 Project proposals due

Mar 31

slides



video Learning Representations II Deep belief networks

ICA

CCA Deep Belief Nets paper, Hinton & Salakhutdinov

CCA Tutorial, M. Borga



Apr 5

slides



video Learning Representations III Fisher Linear Discriminant

Latent Dirichlet Allocation

Intro to Kernel Functions Bishop Ch. 6.1 (required)

Bishop Ch. 6.2, 6.3 (optional)

Apr 7

slides



video Kernel Methods and SVM's Regression: Primal and Dual forms

Kernels and Kernel Regression

SVMs Bishop Ch. 6.1

Bishop Ch. 7, through 7.1.2

Apr 12

slides



video SVM's II Maximizing the margin

Noise and soft margin SVM's

PAC learning and SVM's

Hinge loss, log loss, 0-1 loss Bishop Ch. 7, through 7.1.2 Project midway report due

Apr 14 No CMU classes today

Apr 19

slides



video Active Learning Guest lecture: Dr. Burr Settles Uncertainty sampling

Query by committee Settles: Active learning survey

Apr 21

slides



video ML in Computational Biology Guest lecture: Prof. Ziv Bar-Joseph

Apr 26

slides



video Reinforcement Learning I Markov Decision Processes

Value Iteration

Q learning Kaelbling et al.: Reinforcement Learning: A Survey

Apr 28

RL slides

Final study guide



video Reinforcement Learning 2 Q learning in non-deterministic domains

RL as model for learning in animals

Final exam review