CS66 Machine Learning

Announcements

Final ProjectPapers and Slides

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Schedule

WEEK	DATE	ANNOUNCEMENTS	TOPIC & READING	ASSIGNMENTS
1	Sep 05	Video Intro, DTrees Notes Intro, DTrees	Introduction to Machine Learning and Decision Trees Mitchell: Ch.1 and 3; CIML: Ch. 1; Alpaydin: Ch 1 and 9 Machine Learning by Tom Dietterich in Nature Encyclopedia of Cognitive Science, 2003 (skim Sect 4-8)	Homework 1: Decision Trees Lab 2 Exercise
	Sep 07	Video, Notes
2	Sep 12	Video, Notes
	Sep 14	Video, Notes Drop/add ends (Sep 15)	Instance-Based Learning CIML: Ch 3 (except 3.4); Mitchell: Ch. 8 Curse of Dimensionality
3	Sep 19	Video, Notes		Project 1: Decision Trees
	Sep 21	Video, Notes	Evaluation Methodology & Practical Considerations CIML: Ch. 2 and 5; Mitchell: Ch. 5 Recommended: 8.3 and 8.4 of Manning et al. for more depth on PR, ROC, F1, etc.
4	Sep 26	Video, Notes
	Sep 28	Video, Notes
5	Oct 03	Video, Notes	Probabilistic Models: Naive Bayes and Logistic Regression Basic probability definitions Mitchell Supplement: Estimating Probabilities Mitchell Supplement: Generative And Discriminative Classifiers: Naive Bayes And Logistic Regression CIML: Ch. 9 Optional: On Discriminative vs. Generative classifiers by Andrew Ng and Michael I. Jordan, NIPS, 2001. Optional: Derivation of Naive Bayes as Logistic Regression (also see Michell Supplement above)	Homework 2: KNN and Evaluation
	Oct 05	Video, Notes
6	Oct 10	Video, Notes Video, Notes from Lab Wrapup		Project 2: Probabilistic Models
	Oct 12	Midterm exam - in class
	Oct 17	Fall Break
	Oct 19
7	Oct 24	Video, Notes	Regularization; Bias-Variance Tradeoff Mitchell Supplement: Ch 3.3 from last week's reading CIML: Ch. 5.9, Ch 7.1-7.3 (regularization and hyperparameter discussion only)
	Oct 26	Video, Notes	Ensemble Learning Methods CIML: Ch. 13 Ensemble Methods in Machine Learning by Tom Dietterich Optional: Explaining the Success of AdaBoost and Random Forests as Interpolating Classifiers by Wyner, Olson, Bleich, and Mease, JMLR, 2017
8	Oct 31	Video, Notes
	Nov 02	Video, Notes	Support Vector Machines and Kernels A User's Guide to Support Vector Machines by Asa Ben-Hur and Jason Weston CIML: Ch. 7.7 and Ch. 11	Homework 3: Probabilistic Models and Bias-VarianceKNN
9	Nov 07	Video, Notes
	Nov 09	Video, Notes Last day to declare CR/NC or withdraw (Nov 10)	Unsupervised Learning; Dimensionality Reduction CIML: Ch 3.4, Ch. 15	Project 3: SVMs and Ensembles
10	Nov 14	Video, Notes
	Nov 16	Video, Notes	Semi-supervised (and other) Approaches Semi-Supervised Learning Literature Survey by Jerry Zhu. We will cover aspects of Ch 1-5 and 11.	Final Project
11	Nov 21	Video, Notes
	Nov 23	Thanksgiving
12	Nov 28	Video, Notes	Bayesian Networks An Introduction to Graphical Models by Kevin Murphy Optional: Ch. 8 Graphical Models in Pattern Recognition and Machine Learning by Charles Bishop. A more technical treatment of the subject.
	Nov 30	Video, Notes
13	Dec 05	Video, Notes
	Dec 07	Midterm exam - in class
14	Dec 12	Video, Notes	Course Wrapup A Few Useful Things to Know about Machine Learning by Pedro Domingos in the Communications of the ACM, 2012 Machine Bias from ProPublica, May 23, 2016 Fairness in Machine Learning by Mortiz Hardt at UC Berkeley
	Dec 21	Final exam slot 9am-12pm (Presentations)

CS66 - Machine LearningFall 2017

Announcements

Schedule

Introduction to Machine Learning and Decision Trees

Instance-Based Learning

Evaluation Methodology & Practical Considerations

Probabilistic Models: Naive Bayes and Logistic Regression

Regularization; Bias-Variance Tradeoff

Ensemble Learning Methods

Support Vector Machines and Kernels

Unsupervised Learning; Dimensionality Reduction

Semi-supervised (and other) Approaches

Bayesian Networks

Course Wrapup

CS66 - Machine Learning
Fall 2017