Johns Hopkins Machine Learning 600.475 Spring 2014: Syllabus

Date	Topics	Readings
Introduction
M 1/27	Machine Learning Foundations Overview, applications, settings	Bishop 1
W 1/29	Math Review Calculus, linear algebra, optimization	Linear algebra Bishop Appendix C
M 2/3	Probability Probability, stats	You do NOT need to read all of these. However, you should be familiar with the material. Probability: Bishop Chapter 2 Bishop Appendix B Andrew Moore Tutorial Tom Minka's nuances of probability (advanced) Wolfram Probability and stats
W 2/5	Decision Trees Construction, pruining, over-fitting	Nilsson chapter 6
Supervised Learning: Linear Methods
M 2/10	Regression Least squares and regression	Bishop 3
W 2/12	Classification Logistic Regression	Bishop 4
M 2/17	Generative vs. discriminative Naive Bayes and Logistic Regression	Ng and Jordan, 2001
W 2/19	Online methods Perceptron, multi-class, structured	Blum. On-Line Algorithms in Machine Learning. 1996 Discriminative Training Methods for Hidden Markov Models: Theory and Experiments with Perceptron Algorithms. EMNLP, 2002. Bishop 4.1.2 Reducing Multiclass to Binary (Sections 1, 2, 3)
Supervised Learning: Non-Linear Methods
M 2/24	Support Vector Machines Max-margin classification and optimization	Bishop 7.1 Chris Burges SVM Tutorial Bishop Appendix E
W 2/26	Kernel Methods Dual optmization, kernel trick	Bishop 6.1, 6.2
M 3/3	Instance based learning Nearest-neighbors	Bishop 2.5 Mitchell 8-8.4
W 3/5	Neural Networks Neural Network models	Bishop 5.1,5.2,5.3,5.5
M 3/10	Ensemble Methods Boosting	Bishop 14.1,14.2,14.3 A Short Introduction to Boosting
Unsupervised Learning
W 3/12	Clustering Expectation-Maximization and k-means	Bishop 9
M 3/24	EM and Clustering 1 Gaussian mixture models	Bishop 9
W 3/26	EM and Clustering 2 The EM Algorithm	Bishop 9
Graphical Models
M 3/31	Graphical models 1 Bayesian networks and conditional independence	Bishop 8.1, 8.2
W 4/2	Graphical models 2 MRFs and Exact inference	Bishop 8.3, 8.4
M 4/7	Graphical models 3 Inference	Bishop 8.3, 8.4
W 4/9	Sequential graphical models 1 Max Sum and Max Product	Bishop 13.1,13.2
M 4/14	Sequential graphical models 2 HMMs and CRFs	Sutton, McCallum CRF tutorial
Other Topics
W 4/16	Current Trends in Supervised Learning
M 4/21	TBD
W 4/23	Dimensionality reduction PCA, probabilistic PCA, LDA	Bishop 12.1,12.2,12.3 Max Welling's PCA Tutorial Jonathon Shlens's PCA Tutorial
M 4/28	Practical Machine Learning
W 4/30	Semi-supervised learning Guest Lecture	Jerry Zhu Semi-Supervised Learning Tutorial
W 5/14	9-12pm Final Poster Session Time Project presentations