Semester:  Fall 2017, also offered on Fall 2020, Spring 2020, Spring 2018 and 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 
Tue, Sep 19 
Lecture 9: model selection (finite hypothesis class) Refs: [1] (not mandatory to be read) 

Thu, 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  —  
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  — 