Semester:  Fall 2017, also offered on Fall 2016 
Time and place:  Tuesday and Thursday, 12pm1.15pm, Wetherill Lab 320 
Instructor:  Jean Honorio, Lawson Building 2142J (Please send an email for appointments) 
TAs: 
Chang Li, email: li1873 at purdue.edu, Office hours: Monday, noon2pm, HAAS G50 Adarsh Barik, email: abarik at purdue.edu, Office hours: Wednesday, 1:203:20pm, HAAS G50 
Date  Topic (Tentative)  Notes 
Tue, Aug 22  Lecture 1: perceptron (introduction)  Homework 0: due on Aug 24 at beginning of class  NO EXTENSION DAYS ALLOWED 
Thu, Aug 24  Lecture 2: perceptron (convergence), maxmargin classifiers, support vector machines (introduction)  Homework 0 due  NO EXTENSION DAYS ALLOWED 
Tue, Aug 29  Lecture 3: nonlinear feature mappings, kernels (introduction), kernel perceptron  Homework 0 solution 
Thu, Aug 31 
Lecture 4: SVM with kernels, dual solution Refs: [1] [2] (not mandatory to be read) 
Homework 1: due on Sep 7, 11.59pm EST 
Tue, Sep 5 
Lecture 5: oneclass problems (anomaly detection), oneclass SVM, multiway classification, direct multiclass SVM Refs: [1] [2] [3] [4] (not mandatory to be read) 

Thu, Sep 7 
Lecture 6: rating (ordinal regression), PRank, ranking, rank SVM Refs: [1] (not mandatory to be read) 
Homework 1 due 
Tue, Sep 12  Lecture 7: linear and kernel regression, feature selection (information ranking, regularization, subset selection)  
Thu, Sep 14  Lecture 8: ensembles and boosting  Homework 2: due on Sep 21, 11.59pm EST 
Thu, Sep 19 
Lecture 9: model selection (finite hypothesis class) Refs: [1] (not mandatory to be read) 

Tue, Sep 21  —  Homework 2 due 
Tue, Sep 26 
Lecture 10: model selection (growth function, VC dimension, PAC Bayesian bounds) Notes: [1] 

Thu, Sep 28 
Lecture 11: performance measures, crossvalidation, biasvariance tradeoff, statistical hypothesis testing Notes: [1] 

Tue, Oct 3 
Lecture 12: dimensionality reduction, principal component analysis (PCA), kernel PCA Notes: [1] 

Thu, Oct 5  —  Project plan due (see Assignments for details) 
Tue, Oct 10  OCTOBER BREAK  
Thu, Oct 12  —  
Tue, Oct 17  MIDTERM (lectures 1 to 11)  12pm1.15pm, Wetherill Lab 320 
Thu, Oct 19  (midterm solution)  Homework 3: due on Oct 26, 11.59pm EST 
Tue, Oct 24 
Lecture 13: generative probabilistic modeling, maximum likelihood estimation, mixture models, EM algorithm (introduction) Notes: [1] 

Thu, Oct 26 
Lecture 14: mixture models, EM algorithm, convergence, model selection Notes: [1] 
Homework 3 due 
Tue, Oct 31 
Lecture 15: active learning, kernel regression, Gaussian processes Refs: [1] (not mandatory to be read) 

Thu, Nov 2 
Lecture 16: collaborative filtering (matrix factorization), structured prediction (maxmargin approach) Notes: [1] Refs: [1] (not mandatory to be read) 

Tue, Nov 7 
Lecture 17: Bayesian networks (motivation, examples, graph, independence) Notes: [1] Refs: [1] [2] (not mandatory to be read) 

Thu, Nov 9 
Lecture 18: Bayesian networks (independence, equivalence, learning) Refs: [1] [2] [3, chapters 1620] (not mandatory to be read) 
Preliminary project report due (see Assignments for details) 
Tue, Nov 14 
Lecture 19: Bayesian networks (introduction to inference), Markov random fields, factor graphs Refs: [1] [2] (not mandatory to be read) 

Thu, Nov 16 
Lecture 20: Markov random fields (inference, learning) Refs: [1] [2] [3, chapters 1620] (not mandatory to be read) 

Tue, Nov 21  Lecture 21: Markov random fields (inference in general graphs, junction trees)  
Thu, Nov 23  THANKSGIVING VACATION  
Tue, Nov 28  TBD  
Thu, Nov 30  FINAL EXAM (lectures 12 to 21)  12pm1.15pm, Wetherill Lab 320 
Sat, Dec 2  —  Final project report due (see Assignments for details) 
Tue, Dec 5  —  
Thu, Dec 7  — 