STA561: Probabilistic Machine Learning: Fall 2013
||OH: Fri 2-3||Gross 318|
|TAs:||Kevin Luo ||email@example.com||OH:||Friday 10-11||North 225|
|Jordan Hashimi ||firstname.lastname@example.org||OH:||Thurs 3-4||Gross Hall, 318|
|Nick Jarrett ||email@example.com||OH:||Wed 7-9pm||SECC (Old Chemistry 211A)|
|Wenzhao Lian ||firstname.lastname@example.org||OH:||Thurs 1-2||Gross Hall, 351|
|Class:||M/W 10:05-11:20am || Westbrook 0012, Divinity School |
Introduction to machine learning techniques. Graphical models, latent variable models, dimensionality reduction techniques, deep learning, regression, kernel methods, state space models, HMMs, MCMC, variational methods. Emphasis is on applying these techniques to real data in a variety of application areas.
News and information
CS Students: If you are taking this course to fulfill your CS AI/ML Quals requirement, then you will be required, without exception, to take a written final exam, and this final will be worth 35% of your grade (the other proportions will be shuffled around to accommodate this change). You will not be exempt from the other course requirements.
- Homework 4 is not due until 10/7. However, we are writing up a part B of the homework and we have posted it on Sakai; both parts are now due 10/7/2013 before class.
- The take-home midterm will go out on 10/7.
- Scribes for the next part of the class: please sign up again on Sakai. We are putting three people on each set of notes.
- Survey sent around about which poster session you will go to.
- The project proposal (single page PDF) is due on Sakai on Wednesday October 2nd before class. We have outlined exactly what should go into this proposal. Please use this template and talk to us if you have questions or ideas!
All students: we will have two poster sessions: December 4th (Wednesday) from 2-5pm and December 14th (Saturday) from 2-5pm (both in Gross Hall 3rd floor East Meeting Space). Come to one and only one of these sessions. I highly recommend coming to the first. If you are in the above category of CS students taking this class for credit, you will be able to take the final exam during either time period. We will send around a survey so you can sign up for one of the two dates. If you are auditing the course, we'd love to have you at the first poster session (bring your research groups too!).
Statistics at the level of STA611 (Introduction to Statistical
Methods) is encouraged, along with knowledge of linear algebra and
Course grade is based on homeworks (45%), take home midterm (15%), a
final project (30%), and class participation and scribe notes (10%).
Homeworks are due to me exactly one week after they are handed out at
the beginning of class. Solutions should be uploaded to SAKAI before
class on the due date; they should be each a single PDF document, and
additional files will not be considered. I recommend using LaTeX to
write up your homeworks (here is a homework template for you to use); if
you have never used LaTeX before, you should consider this course an
excellent opportunity to learn how to use it. Late homeworks will not
be accepted, although you are allowed one late homework (maximum one
week) for the course. Students may (and should) collaboratively
discuss the homework assignments; however, I expect each student to
program and write up their own homework solutions. Please write the
names of the students you discussed the homework assignment with at
the top of your solutions, and cite any material used in the
preparation of the homeworks.
There is a Piazza course
discussion page. Please direct questions about homeworks and other
matters to that page. Otherwise, you can email the instructors (TAs
and professor) at email@example.com. Note that we are more likely to
respond to the Piazza questions than to the email, and your classmates
may respond too, so that is a good place to start.
Each lecture will have up to four scribes, who will type up notes in
the LaTeX template. Within a week of class,
the scribes should send the TAs the LaTeX file, at which point we will
edit them and post them to the website. It is best to discuss
beforehand with your fellow scribes what role you will take in the
write-up: Careful note-taker, tex writer, figure maker, and copy
editor, or whether you will split these roles among the four of
you. If you have never used LaTeX before, there are online
GUIs, and even online
compilers that might help you. Here is an example of well-done scribe notes and associated figure.
The course project will include a project proposal due mid-semester, a
four page writeup of the project at the end of the semester, and an
all-campus poster session where you will present your work. This is
the most important part of the course; we strongly encourage you to
come and discuss project ideas with us early and often throughout the
semester. We expect some of these projects to become publications. You
are absolutely permitted to use your current rotation or research
project as course projects.
A second set of references for R may be useful. First, you can
download R from the CRAN
website. There are many resources, such as R Studio, that can help with the
programming interface, and tutorials
on R are all over the place. If you are getting bored with the
standard graphics package, I really like using ggplot2 for beautiful graphics and
figures. Finally, you can integrate R code and output with plain text
using KNITR, but that might be
going a bit too far if you are a beginner.
The course will follow Kevin Murphy's Machine Learning: a probabilistic approach book. I may include optional readings or videos as appropriate.
Some other texts and notes that may be useful include:
|Michael Lavine, Introduction to Statistical Thought (an introductory statistical textbook with plenty of R examples, and it's online too)
||Chris Bishop, Pattern Recognition and Machine Learning
||Daphne Koller & Nir Friedman, Probabilistic Graphical Models
||Hastie, Tibshirani, Friedman, Elements of Statistical Learning (ESL) (PDF available online)
||David J.C. MacKay Information Theory, Inference, and Learning Algorithms (PDF available online)
The final project TeX template and final project style file should be used in preparation of your final project report. Please follow the instructions and let me know if you have questions. We will have a poster session where you present your research project in lieu of a final exam.
This syllabus is tentative, and will almost surely be modified. Reload your browser for the current version.
- (August 26th) Introduction: concepts in probability and statistics [Scribe notes]
- (August 28th) Introduction: MLE, MAP, Bayesian reasoning [Scribe notes]
- (September 2nd) Introduction: exponential family, conjugacy, and sufficiency [Scribe notes]
- (September 4th) Simple discrete models: chains, trees, hierarchical models [Scribe notes]
- (September 9th) Gaussian models [Scribe notes]
- (September 11th) Linear Regression [Scribe notes]
- Required: (reading) MLPP Ch 7
- Optional: Metacademy -- Linear Regression (I particularly recommend the chapter of Hastie, Tibshirani, Friedman)
- (September 16th) Generalized linear models [Scribe notes]
- (September 18th) Mixture models & K-means & Expectation Maximization [Scribe notes]
- (September 23th) Hidden Markov models [Scribe notes]
- (September 25th) Hidden Markov models (continued) and State Space models [Scribe notes]
- (September 30th) Exact inference [Scribe notes]
- Required: (reading) MLPP Ch 20
- Optional: (reading) MacKay: Chapter 16 (Message passing), Chapter 21 (Exact Inference by Complete Enumeration), Chapter 24 (Exact Marginalization), Chapter 26 (Exact Marginalization in Graphs)
- Optional: Metacademy -- Variable elimination
- (October 2nd) Factor analysis [Scribe notes]
- (October 7th) Sparse linear models [Scribe notes]
- Required: (reading) MLPP Ch 13
- Optional: (reading) Elements of Statistical Learning (ESL) Chapter 3: Linear models for regression
- Optional: Metacademy -- LASSO
- (October 9th) Kernels and kernel methods [Scribe notes]
- (October 16th) Gaussian processes [Scribe notes]
- Required: (reading) MLPP Ch 15
- Optional: (reading) GPML, Chapter 3: Gaussian process regression
- Optional: (reading) GPML, Chapter 4: Gaussian process classification
- Optional: (video) Gaussian processes Karl Rasmussen
- Optional: (video) Gaussian Process Basics David MacKay
- Optional: Metacademy -- Gaussian Processes
- (October 21st) Markov random fields
- Required: (reading) MLPP Ch 19
- Optional: (reading) ESL Chapter 17: Undirected Graphical Models
- Optional: Metacademy -- Markov Random Fields
- (October 23rd) MCMC introduction: importance sampling, rejection sampling, simulated annealing, MCs
- (October 28th) MCMC: Metropolis hastings, Gibbs sampling
- (October 30th) Variational algorithms: duality, mean field
- (November 4th) Variational algorithms: BP, EP, convex relaxations, VB [Scribe notes]
- (November 6th) Latent Dirichlet allocation & topic models [no scribe notes; see slides in Sakai and David Blei's website for details]
- Required: (reading) MLPP Ch 27
- Optional: (reading) David M. Blei, Andrew Ng and Michael I. Jordan. Latent Dirichlet allocation. Journal of Machine Learning Research 3:993-1022, 2003.
- Optional: (reading) David M. Blei and John D. Lafferty. Topic Models. Text Mining: Classification, Clustering and Applications, 2009.
- Optional (reading) Thomas L. Griffiths and Mark Steyvers. Finding Scientific Topics. Proceedings of the National Academy of Sciences 101:5228-5235, 2004.
- Optional: (video) David M. Blei Topic Models
- Optional: Metacademy -- Latent Dirichlet Allocation
- (November 11th) Dirichlet process mixture models
- (November 13th) Review class [Unedited lecture notes]
- (November 18th) Adaptive basis function learning (CART and Boosting)
- (November 20th) Deep belief learning
- (November 25th) Graphical model structure learning