Johns Hopkins Machine Learning 601.475 Fall 2020: Syllabus

Syllabus

Readings are primarily from the course textbook Machine Learning: a Probabilistic Perspective by Kevin Murphy. Readings from the textbook will be prefixed by "M". For example, M1 means Murphy chapter 1. Unless otherwise notes, skip sections that have a * in the title. These are optional.

For those who prefer Chris Bishop's Pattern Recognition and Machine Learning, I will list the corresponding readings from this textbook. These readings will be prefixed by "B". Optional readings will be written in italics.

Recitation sections are led by the TAs and are optional.

Date	Topics	Readings
Supervised Learning: Classifiers
M 8/31	Machine Learning: Overview Background of the field	M1 PDF B1 If you need to review relevant math, do it now.
W 9/2	Machine Learning: Foundations Introduce key concepts
F 9/4	Recitation: Probability/Lin Alg Events, random variables, probabilities, pdf, pmf, cdf, mean, mode, median, variance, multivariate distributions, marginal\ s, conditionals, Bayes theorem, independence
M 9/7	NO CLASS
W 9/9	Regression 1 Linear regression	M7, excluding M7.4 and M7.6 B3 Those without a probability background should read M2.1-2.4.1 Other probability background readings: B2, B Appendix B, Andrew Moore Tutorial, Tom Minka's nuances of probability
F 9/11	Recitation: Math Review Basic properties of matrices, eigenvalue decompositions, singular value decompositions	cs229's linear-algebra notes Bishop's appendix on Lagrange Multipliers
M 9/14	Regression 2
W 9/16	Classification 1 Introduces logistic regression and classification	M8 (stop at M8.3.7), M13.3 B4
F 9/18	Recitation: MLE
M 9/21	Data Geometry: Support Vector Machines Max-margin classification and optimization	M14.5 Andrew Ng's notes on SVMs B7.1
M W/23	Data Geometry: Kernel Methods Dual optimization, kernel trick	M14.1, M14.2 B6.1, B6.2
F 9/25	Recitation: Convex Optimization
M 9/28	Perceptron Online learning algorithms	M8.5
W 9/30	Deep Learning: Shallow Learning	M16.5, M27.7, M28 B5.1, 5.2, 5.3, 5.5
F 10/2	Recitation: PyTorch Intro
M 10/5	Deep Learning: Backprop
W 10/7	Deep Learning: The Details
F 10/9	Recitation: Midterm Review
M 10/12	Decision Trees Construction, pruning, over-fitting	M2.8, M16.2 Tom Mitchell's notes on decision trees
W 10/14	Boosting Ensemble methods	M16.4, M16.6 A Short Introduction to Boosting B14.1, B14.2, B14.3
F 10/16	Recitation: Midterm (Required Attendance)
Unsupervised Learning: Core Methods
M 10/19	Clustering K-means	M25.1, M11 (stop at M11.4) B9
W 10/21	TBD
F 10/23	No Recitation: Fall Break
M 10/26	Tentative: Midterm
W 10/28	Expectation Maximization 1	M11.4 (stop at M11.4.8) Andrew Ng's notes on EM
F 10/30	Recitation: EM
M 11/2	Expectation Maximization 2
W 11/4	Graphical Models 1 Bayesian networks and conditional independence	M10 B8.1, B8.2
F 11/6	Recitation: Graphical Models
M 11/9	Graphical Models 2 MRFs and exact inference	M19.1 (stop at M19.4), M19.5 B8.3, B8.4
W 11/11	Graphical Models 3 Inference	M20 (stop at M20.3)
F 11/13	Recitation: Graphical Models
M 11/16	Graphical Models 4 Max Sum and Max Product
W 11/18	Structured Prediction 1 Margin based methods, HMMs, CRFs	M17 (stop at M17.6), M19.6, M19.7 Sutton, McCallum CRF tutorial B13.1, 13.2
F 11/20	Recitation: Transformers
M 11/30	Structured Prediction 2 Recurrent Neural Networks
W 12/2	Dimensionality reduction PCA	M12.2 B12.1, B12.2, B12.3
F 12/4	Recitation: Dimensionality Reduction
M 12/7	Fairness, Accountability, Transparency and Ethics of ML
W 12/9	Practical Machine Learning
F 12/11	No Recitation
TBD	Final Exam TBD (75 minutes)