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 


Andrew Moore's Basic Probability Tutorial
Bishop: Ch. 1 thru 1.2.3
Bishop: Ch 2 thru 2.2


Jan 20 

 Conditional independence
 Multinomial Naive Bayes

Mitchell:
Naive Bayes and Logistic Regression


Jan 25 

 Gaussian Bayes classifiers
 Document classification
 Brain image classification
 Form of decision surfaces

Mitchell:
Naive Bayes and Logistic Regression

HW1 due
HW2 out

Jan 27 

 Naive Bayes  the big picture
 Logistic Regression: Maximizing conditional likelihood
 Gradient ascent as a general learning/optimization method

Mitchell:
Naive Bayes and Logistic Regression
Ng & Jordan: On
Discriminative and Generative Classifiers, NIPS, 2001.


Feb 1 

 Generative/Discriminative models
 minimizing squared error and maximizing data likelihood
 biasvariance decomposition
 regularization



Feb 3 
Practical Issues

 Feature selection
 Overfitting
 BiasVariance tradeoff



Feb 8 

 Bayes nets
 representing joint distributions with conditional independence assumptions


HW3 out

Feb 15 

 Dseparation and Conditional Independence
 Inference
 Learning from fully observed data
 Learning from partially observed data



Feb 17 


EM
and HMM tutorial J. Bilmes


Feb 22 

 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 


Mitchell: Ch. 7 

Mar 1 



HW4 due

Mar 3 
Midterm Exam 
 in class
 open notes, open book, no internet


Midterm
Solution

Mar 15 

 Mistake bounds
 Weighted Majority Algorithm

Mitchell: Ch. 7 

Mar 17 

 CoTraining / Multiview Learning
 Never ending learning (NELL)



Mar 22 

 Markov models
 HMM's and Bayes Nets
 Other probabilistic time series models

Bishop Ch. 13 

Mar 24 

 Nonlinear regression
 Backpropagation and Gradient descent
 Learning hidden layer representations

Mitchell Ch. 4 Bishop Ch. 5 
Project proposals due 
Mar 29 

 Artificial neural networks
 PCA

Bishop Ch. 12 through 12.1
A Tutorial on PCA, J. Schlens
SVD and PCA, Wall et al.


Mar 31 

 Deep belief networks
 ICA
 CCA

Deep Belief Nets paper, Hinton
& Salakhutdinov
CCA Tutorial, M. Borga


Apr 5 

 Fisher Linear Discriminant
 Latent Dirichlet Allocation
 Intro to Kernel Functions

Bishop Ch. 6.1 (required)
Bishop Ch. 6.2, 6.3 (optional)


Apr 7 

 Regression: Primal and Dual forms
 Kernels and Kernel Regression
 SVMs

Bishop Ch. 6.1
Bishop Ch. 7, through 7.1.2


Apr 12 

 Maximizing the margin
 Noise and soft margin SVM's
 PAC learning and SVM's
 Hinge loss, log loss, 01 loss

Bishop Ch. 7, through 7.1.2

Project midway report due 
Apr 14 

No CMU classes today 


Apr 19 

Guest lecture: Dr. Burr Settles
 Uncertainty sampling
 Query by committee

Settles: Active learning survey 

Apr 21 

Guest lecture: Prof. Ziv BarJoseph 


Apr 26 

 Markov Decision Processes
 Value Iteration
 Q learning

Kaelbling et al.: Reinforcement Learning: A Survey 

Apr 28 

 Q learning in nondeterministic domains
 RL as model for learning in animals
 Final exam review



May 6 (Friday) 
Final Exam 
 14pm
 Location: Gates Hillman 4401
 open notes, open book, no internet

Final study guide 
