Machine Learning
10701/15781, Fall 2011Eric Xing School of Computer Science, CarnegieMellon University 
Syllabus and (tentative) Course Schedule
Date  Lecture  Topics  Readings and useful links 
Handouts 

Module 1  
Intro to Functional Approximation  
Mon 9/12  Lecture 1: Overview. Slides, (Annotated Slides) 
Overview of Machine Learning
Decision tree learning 


Wed 9/14  Lecture 2: Nonparametric methods. Slides, (Annotated Slides) 
Non parametric learning methods



Preliminaries: Learning linear separation functions  
Mon 9/19  Lecture 3: Generative versus discriminative classifers.
Slides , (Annotated Slides) 


HW 1 out 
Wed 9/21  Lecture 4: Linear regression and sparsity. Slides (Annotated Slides) 



Into the nonlinear world and theoretical foundations of supervised learning  
Mon 9/26  Lecture 5: Neural Networks Slides (Annotated Slides) 
Neural networks and deep learning 


Wed 9/28  Lecture 6: Computational Learning Theory
Slides (Annotated Slides) 
Computational and Learning theory



Mon 10/3 
Lecture 7: Overfitting and model selection Slides (Annotated Slides) 
Overfitting and model selection 

HW 1 due, HW 2 and data out 
Unsupervised learning: Clustering  
Wed 10/5 
Lecture 8: Clustering Slides(Annotated Slides) 
Clustering



Mon 10/10 
Lecture 9: Expectation Maximization Slides (Annotated Slides) 
Probabilistic models for clustering: Expectationmaximization 


Wed 10/12 
Lecture 10: Infinite Mixture Models Slides (Annotated Slides) 
Infinite Clusters



Structured Inference: Graphical Models  
Mon 10/17  Lecture 11: Hidden Markov Models Slides (Annotated Slides) 
Sequential Labeling: Hidden Markov Model 

HW 2 due, HW 3 out, Project proposal due 
Wed 10/19  Lecture 12: Conditional Random Fields Slides (Annotated Slides)  Conditional Random Field: a discriminative HMM 


Mon 10/24  Lecture 13: Bayesian Networks Slides (Annotated Slides)  Bayesian Networks



Wed 10/26  Midterm Exam  open book, open notes, no computers  Will cover lectures upto 10/19  
Mon 10/31  Lecture 14: Inference and Learning for Bayesian Networks Slides (Annotated Slides)  Inference and Learning for Bayesian Networks  HW 3 due 

Wed 11/2  Lecture 15: Undirected Graphical Models and Approximate Inference Slides (Annotated Slides)  Undirected Graphical Models and Approximate Inference 

HW 4 out 
Alternative strategies of learning  
Mon 11/7  Lecture 16: PCA versus Topic models
Slides (No annotated slides) 
Subspace learning: nonprobabilistic vs probabilistic approaches



Wed 11/9  Lecture 17: Support Vector Machines Slides (Annotated Slides)  Support Vector Machines 


Applications  
Mon 11/14  Lecture 18: Structured Sparsity in Genetics (Lecturer: Seyoung Kim)
Slides 
Structured sparsity in genetics  Project midterm report due 

Wed 11/16  Lecture 19: Generative Latent Variable Models of Text (Lecturer: Jacob Eisenstein) Slides  Social media modeling and analysis via latent space models  HW 4 due, HW 5 out  
Advanced Topics  
Mon 11/21  Lecture 20: Advanced topics in maximummargin learning
Slides (Annotated Slides) 


Wed 11/23  No class for Thanksgiving Break 

Mon 11/28  Lecture 21: Maxmargin learning of graphical models
Slides (Annotated Slides) 


Wed 11/30  Lecture 22: Ensemble Methods: Boosting from weak learners
Slides (Annotated Slides) 
Boosting: ensemble of weak learners  
Mon 12/5  Lecture 23: Reinforcement Learning
Slides 
Reinforcement Learning
Review

HW 5 due  
Wed 12/7  No class!  No class!  
Thu 12/8  Poster session  NSH atrium 2:306:30pm  Project final report due  
Tue 12/13  Final Exam  Doherty Hall 2210, 1:004:00pm  One A4 sheet of paper allowed, Closed book, CLosed notes. 
© 2008 Eric Xing @ School of Computer Science, Carnegie Mellon University
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