SP14 CS536 ML Syllabus (original) (raw)

[Spring 2014 –16:198:536] Machine Learning

Schedule / Syllabus (Subject to Change)

Lecture # Day Date Topic Readings Notes
1 Tue Jan 21 Introduction � Murphy Ch. 1 � Bishop Ch. 1 � http://www.cs.cmu.edu/~tom/pubs/MachineLearning.pdf Optional � Mitchell Ch. 1
2 Thu Jan 23 Linear Regression & Bias-variance Tradeoff � Murphy Ch. 7.1-7.5 � Bishop Ch. 3.1-3.2 Optional � HTF Ch. 3 & 4
3 Tue Jan 28 Overfitting, Regularization, Sparsity, & Evaluation � Murphy Ch. 7.1-7.5 � Bishop Ch. 3.1-3.2 � http://www.autonlab.org/tutorials/overfit10.pdf � http://citeseer.ist.psu.edu/viewdoc/summary?doi=10.1.1.37.3325 Optional � HTF Ch. 3 & 4
4 Thu Jan 30 Logistic Regression & Na�ve Bayes � Murphy Ch. 5 & 8 � Bishop Ch. 4
5 Tue Feb 4 Gaussian Na�ve Bayes & Generative vs. Discriminative Classifiers � http://www.cs.cmu.edu/~tom/mlbook/NBayesLogReg.pdf Optional: � http://ai.stanford.edu/~ang/papers/nips01-discriminativegenerative.pdf � http://select.cs.cmu.edu/class/10701-F09/readings/bag-of-words.pdf HW #1 out.
6 Thu Feb 6 Decision Trees & IBL � http://eliassi.org/Nilsson-ch6-ml.pdf � Murphy Ch. 16 � HTF 13.3-13.5 Optional: � Mitchell Ch. 3 & 8
7 Tue Feb 11 Ensemble Methods � http://eliassi.org/boosting-schapire.pdf � http://www.springerlink.com/content/u0p06167n6173512/ � Murphy Ch. 16 � Bishop Ch. 14 Optional � HTF Ch. 10, 15, 16
8 Thu Feb 13 No class (snow day) HW #1 due.
9 Tue Feb 18 Ensemble Methods & Perceptron � https://www.cs.princeton.edu/~schapire/papers/explaining-adaboost.pdf
10 Thu Feb 20 Artificial Neural Networks & Deep Learning � Bishop Ch. 4.1.7 & 5 � Murphy Ch. 28 Optional � Mitchell 4 � HTF 11 HW #2 out.
11 Tue Feb 25 SVM & Kernels � http://research.microsoft.com/en-us/um/people/cburges/papers/SVMTutorial.pdf � http://select.cs.cmu.edu/class/10701-F09/readings/hearst98.pdf � http://www.isn.ucsd.edu/pubs/nips00_inc.pdf � Bishop Ch. 6 & 7 � Murphy Ch. 14 Optional � HTF 6, 12.
12 Thu Feb 27 Sample Complexity, PAC, & VC Dimension � http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=788640 � http://eliassi.org/COLTSurveyArticle.pdf � http://jmlr.org/papers/volume6/langford05a/langford05a.pdf � http://www.cs.princeton.edu/courses/archive/spr08/cos511/scribe_notes/0227.pdf � Bishop Ch. 7.1.5 � Murphy Ch. 6.5 & 6.6 Optional: � http://www.cs.cmu.edu/~avrim/Papers/survey.pdf � Mitchell 7 � HTF 7 HW #1 graded.
13 Tue Mar 4 Kernel Learning for Structured Prediction (Guest Lecturer: Chetan Tonde) � Reading material in the Resources folder on Sakai HW #2 due.
14 Thu Mar 6 Bayesian Networks � Murphy Ch. 10 � Bishop Ch. 8
15 Tue Mar 11 Midterm Exam (TA will proctor)
16 Thu Mar 13 In-class Proposal Pitches HW #2 graded.
17 Tue Mar 18 Spring Break
18 Thu Mar 20 Spring Break
19 Tue Mar 25 Bayesian Networks & Graphical Models � http://www.cs.cmu.edu/~aarti/Class/10701/readings/graphical_model_Jordan.pdf � http://www.cs.cmu.edu/~aarti/Class/10701/readings/intro_gm.pdf Proposal pitches graded.
20 Thu Mar 27 Clustering � Murphy Ch. 25 � Bishop Ch. 9 Optional: � HTF 14 � http://www.cs.cmu.edu/~dpelleg/download/xmeans.pdf � http://dl.acm.org/citation.cfm?id=1283494 Midterm graded.
21 Tue Apr 1 Gaussian Mixture Models & Expectation Maximization � Murphy Ch. 11 � Bishop Ch. 9
22 Thu Apr 3 Hidden Markov Models � Murphy Ch. 17 � Bishop Ch. 13 � http://www.cs.cmu.edu/~aarti/Class/10701/readings/gentle_tut_HMM.pdf HW#3 out.
23 Tue Apr 8 Latent Variable Models � Murphy Ch. 27
24 Thu Apr 10 Learning on Graphs � http://eliassi.org/papers/ai-mag-tr08.pdf � http://eliassi.org/papers/henderson-sdm10.pdf � http://eliassi.org/papers/henderson-kdd2012.pdf
25 Tue Apr 15 Dimensionality Reduction � http://www.snl.salk.edu/~shlens/pca.pdf � http://www.cs.cmu.edu/~tom/10701_sp11/slides/CCA_tutorial.pdf � Bishop Ch. 12 Optional: � http://www.cs.cmu.edu/~tom/10701_sp11/slides/pca_wall.pdf HW #3 due.
26 Thu Apr 17 1. What Can 20,000 Models Teach Us? & 2. Reliable Differential Dependency Network Analysis (Guest Lecturer: Dr. Alexandru Niculescu-Mizil) � http://www.cs.cornell.edu/~caruana/ctp/ct.papers/caruana.icml04.icdm06long.pdf � http://www.niculescu-mizil.org/papers/calibration.icml05.crc.rev3.pdf � http://www.cs.cornell.edu/~caruana/ctp/ct.papers/caruana.icml06.pdf � http://www.niculescu-mizil.org/papers/owm.pdf � http://arxiv.org/abs/1307.2611
27 Tue Apr 22 Reinforcement Learning � http://www.cs.cmu.edu/~tom/10701_sp11/slides/Kaelbling.pdf � http://www.research.rutgers.edu/~lihong/pub/Li11Knows.pdf Optional: � Mitchell Ch. 13
28 Thu Apr 24 In-class Final Exam (TA will proctor)
29 Tue Apr 29 In-class Project Presentations HW #3 graded.
30 Thu May 1 In-class Project Presentations Last day of class.
-- Tue May 6 -- Project presentations graded.
-- Tue May 13 -- Final exam graded. Project reports due.
-- Thu May 15 -- Project reports graded. Final grades released.