Semester: | Spring 2020, also offered on Fall 2020, Spring 2018, Fall 2017 and Fall 2016 |
Time and place: | Tuesday and Thursday, 10.30-11.45am, Lawson Building B155 |
Instructor: | Jean Honorio, Lawson Building 2142-J (Please send an e-mail for appointments) |
TAs: |
Adarsh Barik, e-mail: abarik at purdue.edu, Office hours: Wednesday, 9am-11am, HAAS G50 Vinith Budde, e-mail: budde at purdue.edu, Office hours: Monday, 1pm-3pm, HAAS G50 |
Date | Topic (Tentative) | Notes |
Tue, Jan 14 | Lecture 1: perceptron (introduction) | Homework 0: due on Jan 16 at beginning of class - NO EXTENSION DAYS ALLOWED |
Thu, Jan 16 | Lecture 2: perceptron (convergence), max-margin classifiers, support vector machines (introduction) | Homework 0 due - NO EXTENSION DAYS ALLOWED |
Tue, Jan 21 | Lecture 3: nonlinear feature mappings, kernels (introduction), kernel perceptron | Homework 0 solution |
Thu, Jan 23 |
Lecture 4: SVM with kernels, dual solution Refs: [1] [2] (not mandatory to be read) |
Homework 1: due on Jan 30, 11.59pm EST |
Tue, Jan 28 |
Lecture 5: one-class problems (anomaly detection), one-class SVM, multi-way classification, direct multi-class SVM Refs: [1] [2] [3] [4] (not mandatory to be read) |
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Thu, Jan 30 |
Lecture 6: rating (ordinal regression), PRank, ranking, rank SVM Refs: [1] [2] (not mandatory to be read) |
Homework 1 due |
Tue, Feb 4 | Lecture 7: linear and kernel regression, feature selection (information ranking, regularization, subset selection) | |
Thu, Feb 6 | — | |
Tue, Feb 11 | Lecture 8: ensembles and boosting | Homework 2: due on Feb 19, 11.59pm EST |
Thu, Feb 13 | Lecture 9: performance measures, cross-validation, bias-variance tradeoff, statistical hypothesis testing | |
Tue, Feb 18 | Lecture 10: model selection (VC dimension, generalization, structural risk minimization) | Homework 2 due on Wed, Feb 19 |
Thu, Feb 20 | Lecture 11: probability review (joint, marginal and conditional probability), independence, maximum likelihood estimation | |
Tue, Feb 25 | Lecture 12: generative probabilistic modeling, maximum likelihood estimation, decision boundary | |
Thu, Feb 27 | Lecture 13: mixture models, EM algorithm, convergence, model selection | Homework 3: due on Mar 3, 11.59pm EST |
Tue, Mar 3 |
Lecture 14: active learning, kernel regression, Gaussian processes Refs: [1] (not mandatory to be read) |
Homework 3 due |
Thu, Mar 5 | Lecture 15: dimensionality reduction, principal component analysis (PCA), kernel PCA | |
Tue, Mar 10 | MIDTERM (lectures 1 to 12) | 10.30am-11.45am, Lawson Building B155 |
Thu, Mar 12 | (midterm solution) |
Project plan due (see Assignments for details) [Word] or [Latex] format |
Tue, Mar 17 | SPRING VACATION | |
Thu, Mar 19 | SPRING VACATION | |
Tue, Mar 24 | (lecture 15 continues) | |
Thu, Mar 26 |
Lecture 16: collaborative filtering (matrix factorization), structured prediction (max-margin approach) Refs: [1] (not mandatory to be read) |
Homework 4: due on Mar 31, 11.59pm EST |
Tue, Mar 31 | — | Homework 4 due |
Thu, Apr 2 |
Lecture 17: Bayesian networks (motivation, examples, graph, independence) Refs: [1] [2] (not mandatory to be read) |
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Tue, Apr 7 |
Lecture 18: Bayesian networks (independence, equivalence, learning) Refs: [1] [2] [3, chapters 16-20] (not mandatory to be read) |
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Thu, Apr 9 |
Lecture 19: Bayesian networks (introduction to inference), Markov random fields, factor graphs Refs: [1] [2] (not mandatory to be read) |
Preliminary project report due (see Assignments for details) - NO EXTENSION DAYS ALLOWED |
Tue, Apr 14 | (lecture 19 continues) | |
Thu, Apr 16 |
Lecture 20: Markov random fields (inference, learning) Refs: [1] [2] [3, chapters 16-20] (not mandatory to be read) |
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Tue, Apr 21 | (lecture 20 continues) | |
Thu, Apr 23 | Lecture 21: Markov random fields (inference in general graphs, junction trees) | Final project report due (see Assignments for details) - NO EXTENSION DAYS ALLOWED |
Tue, Apr 28 | FINAL EXAM (lectures 13 to 21) |
Start: Tuesday April 28, 10.30am EST End: Wednesday April 29, 10.30am EST |
Thu, Apr 30 | — |