CS281: Advanced Machine Learning

Prof. Ryan Adams (OH: Mon 2:30-3:30pm in MD 233)
TF: Eyal Dechter (OH: Thu 1pm in MD 1st Floor Lounge; Section: Thu 2:30-3:30pm in MD 319)
TF: Scott Linderman (OH: Thu 10am in MD 2nd Floor Lounge; Section: Thu 9-10am in MD 221)
TF: Dougal Maclaurin (OH: Mon 10am in MD 334; Section: Fri 10-11am in MD 223)
Time: Monday and Wednesday, 1-2:30pm
Location: Maxwell-Dworkin G115
Contact: (course number) + (hyphen) + (the word "f13") + (hyphen) + (the word "staff") + "seas.harvard.edu"

syllabus | schedule | piazza | assignments | grading | books | faq | dropbox | quizzes

Announcements


Schedule

Subject to change.
Wed 4 Sep 2013

Lecture 1: Introduction to Inference and Learning


Mon 9 Sep 2013

Lecture 2: Simple Discrete Models [ discrete_coins.m | plot_beta.m | beta_coins.m ]

Linear Models

Wed 11 Sep 2013

Lecture 3: Simple Gaussian Models [ plot_bigauss1.m | plot_bigauss2.m ]


Fri 13 Sep 2013

Section 1: Math Review [ notes ]


Mon 16 Sep 2013

Lecture 4: Bayesian Statistics [ quiz ]


Wed 18 Sep 2013

Lecture 5: Linear Regression (Guest Lecturer: Matt Johnson) [ demos ]


Fri 20 Sep 2013

Section 2: Practical Optimization [ notes ]


Mon 23 Sep 2013

Lecture 6: Linear Classifiers [ quiz ]


Wed 25 Sep 2013

Lecture 7: Generalized Linear Models [ quiz ]

Unsupervised Bayesian Modeling

Fri 27 Sep 2013

Section 3: Undirected Graphical Models and Factor Graphs [ notes ]


Mon 30 Sep 2013

Lecture 8: Directed Graphical Models [ quiz ]


Wed 2 Oct 2013

Lecture 9: Mixture Models [ quiz ]


Fri 4 Oct 2013

Section 4: Factor Analysis and PCA [ notes ]


Mon 7 Oct 2013

Lecture 10: Sparse Linear Models [ quiz ]

Inference Procedures

Wed 9 Oct 2013

Lecture 11: Exact Inference [ quiz ]


Fri 11 Oct 2013

Section 5: The Junction Tree Algorithm [ notes ]


Wed 16 Oct 2013

Lecture 12: Variational Inference (Guest Lecturer: Matt Johnson) [ demo | quiz ]


Fri 18 Oct 2013

Section 6: Loopy Belief Propagation


Mon 21 Oct 2013

Lecture 13: Monte Carlo Basics (Guest Lecturer: Finale Doshi-Velez) [ quiz ]


Wed 23 Oct 2013

Midterm Exam


Fri 25 Oct 2013

Section 7: Particle Filtering


Mon 28 Oct 2013

Lecture 14: Markov Chain Monte Carlo [ quiz ]


Wed 30 Oct 2013

Lecture 15: Advanced Markov Chain Monte Carlo [ quiz ]


Fri 1 Nov 2013

Section 8: MCMC Practicalities

Example Models

Mon 4 Nov 2013

Lecture 16: Latent Dirichlet Allocation [ quiz ]


Wed 6 Nov 2013

Lecture 17: State Space Models


Fri 8 Nov 2013

Section 9: Graph Models [ notes ]

Nonparametric Models

Mon 11 Nov 2013

Lecture 18: Kernels [ quiz ]


Wed 13 Nov 2013

Lecture 19: Gaussian Processes [ quiz ]


Fri 15 Nov 2013

Section 10: Practical Gaussian Processes


Mon 18 Nov 2013

Lecture 20: Dirichlet Processes I [ quiz ]


Wed 20 Nov 2013

Lecture 21: Dirichlet Processes II [ quiz ]


Fri 22 Nov 2013

Section 11: Practical Dirichlet Processes

Deep Learning

Mon 25 Nov 2013

Lecture 22: Boltzmann Machines


Mon 2 Dec 2013

Lecture 23: Neural Networks [ quiz ]


Wed 4 Dec 2013

Lecture 24: Advanced Neural Networks [ quiz ]

Thu 5 Dec 2013


Wed 11 Dec 2013


Assignments

For the assignments, you'll get both a .tex file and a .pdf. You'll also need the class file:
harvardml.cls. Examples of usage: example.tex, example.pdf, example-fig.pdf.

Final Project

See document
here for details. For the final report use the NIPS style files available here.

Grading


General Machine Learning Books

Relevant Specialized Books (Optional)


Frequently Asked Questions