EECS 545: Machine Learning
University of Michigan, Winter 2013
Classroom: DOW 1005
Time: MW 10:30am12:00pm
Instructor: Honglak Lee
Instructor office hours: Tuesdays 2pm4pm, 3773 BBB
GSI: Kihyuk Sohn
GSI office hours: Monday 3pm4pm, Friday 2pm3pm, 1637 BBB
Contact: For all questions, please use Piazza (registration required).
NOTE: Please note that this is a tentative syllabus and subject to change.
The goal of machine learning is to develop computer algorithms that can learn from data or past experience to predict well on the new unseen data. In the past few decades, machine learning has become a powerful tool in artificial intelligence and data mining, and it has made major impacts in many realworld applications.
This course will give a graduatelevel introduction of machine learning and provide foundations of machine learning, mathematical derivation and implementation of the algorithms, and their applications. Topics include supervised learning, unsupervised learning, learning theory, graphical models, and reinforcement learning. This course will also cover recent research topics such as sparsity and feature selection, Bayesian techniques, and deep learning. In addition to mathematical foundations, this course will also put an emphasis on practical applications of machine learning to artificial intelligence and data mining, such as computer vision, data mining, speech recognition, text processing, bioinformatics, and robot perception and control. The course will require an openended research project.
* NOTE: Please see the instructor if you do not satisfy the above requirements. In particular, if you haven't taken at least two of linear algebra, multivariate calculus, and probability courses, it is strongly recommended that you finish them first before taking this course.
There will be four or five (approximately biweekly) problem sets to strengthen the understanding of the fundamental concepts, mathematical formulations, algorithms, and the applications. The problem sets will also include programming assignments to implement algorithms covered in the class.
This course offers an opportunity for getting involved in openended research in machine learning. Students are encouraged to develop new theory and algorithms in machine learning, or apply existing algorithms to new problems, or apply to their own research problems. Please talk to the instructor before deciding about the project topic. Students will be required to complete their project proposals, progress reports, poster presentations and the final report.
Check resource page in ctools for more detailed information.
Homework: 30%
Midterm: 30%
Project: 40% (progress report 10%; final project 30%)
* Up to 2% extra credit may be awarded for active class participations.
No  Date 
 Lecture  Topics  Readings and useful Links  Handouts and due dates 
1  1/9  Wed  Introduction and Overview  Introduction  Bishop: Ch 2.1, Appendix B 

2  1/14  Mon  Supervised Learning: regression  Linear regression  Bishop: Ch 3.1; Stanford CS229 note: www.stanford.edu/class/cs229/notes/cs229notes1.pdf  HW1 out 
3  1/16  Wed  No class (to be replaced with a online or makeup lecture) Supervised Learning: regression  Regularized linear regression; Locally weighted linear regression; Kernel regression; Knearest neighbor  Bishop: Ch 3.2, 1.1, 2.5; Stanford CS229 note: www.stanford.edu/class/cs229/notes/cs229notes1.pdf 

 1/21  Mon  No class  No class  MLK day 


4  1/23  Wed  Supervised Learning: classification  Logistic regression; Generalized linear models; Linear discriminant analysis  Bishop: Ch 4.1, 4.3; Stanford CS229 note: www.stanford.edu/class/cs229/notes/cs229notes1.pdf 

5  1/28  Mon  Supervised Learning: classification  Perceptron; Gaussian discriminant analysis; Naive Bayes  Bishop: Ch 4.2; Stanford CS229 note: www.stanford.edu/class/cs229/notes/cs229notes2.pdf  HW1 due, HW2 out 
6  1/30  Wed  Kernel mehods  Kernel methods; kernel regression  Bishop: Ch 6.16.3 

7  2/4  Mon  Kernel methods  Support vector machines  Bishop: Ch 7.1  
8  2/6  Wed  Kernel methods  Support vector machines; convex optimization overview  Bishop: Ch 7.1; Stephen Boyd's lecture notes (available in resources)  project proposal due 
9  2/11  Mon  Kernel methods  Multivariate Gaussian distribution; Bayesian linear regression; Gaussian Processes  Bishop: Ch 2.3, 3.3, 6.4  HW2 due, HW3 out 
10  2/13  Wed  Kernel methods  Gaussian Processes  Bishop: Ch 6.4 

11  2/18  Mon  Regularization and Model Selection  Regularization and Model Selection; Advice on using ML algorithms 
 
12  2/20  Wed  Feature selection  Advice on using ML algorithms; Feature Selection  http://jmlr.csail.mit.edu/papers/volume3/guyon03a/guyon03a.pdf 

13  2/25  Mon  Graphical models  Bayesian Networks  Bishop: Ch 8.1, 8.2  HW3 due, HW4 out 
14  2/27  Wed  Graphical models  Markov Networks  Bishop: Ch 8.3 

 3/4  Mon  No class  No class  winter break 


 3/6  Wed  No class  No class  winter break 


15  3/11  Mon  Graphical models  Inference in graphical models  Bishop: Ch 8.4  Project progress report due 
16  3/13  Wed  Graphical models  Inference in graphical models  Bishop: Ch 9; See also Bishop Ch 2 for basics of maximum likelihood for binary/multinomial/Gaussian variables 

17  3/18  Mon  Graphical models  Learning in graphical models; EM  Bishop: Ch 9; See also Bishop Ch 2 for basics of maximum likelihood for binary/multinomial/Gaussian variables  
18  3/20  Wed  Unsupervised learning  Abstract view of EM; Unsupervised Learning – PCA  Bishop: Ch 9  HW4 due 
19  3/25  Mon  Midterm exam review 
 
20  3/27  Wed  Advanced Unsupervised learning  Nonlinear latent variable models; Deep Learning  Bishop: Ch 12.4  
21  4/1  Mon  Deep Learning  Neural network; Autoencoders; Restricted Boltzmann machines; Deep belief networks  Bengio's survey paper www.iro.umontreal.ca/~bengioy/papers/ftml.pdf  Midterm exam 
22  4/3  Wed  Unsupervised Learning  HMM  Bishop: Ch 13.1, 13.2  
23  4/8  Mon  Reinforcement learning  RL introduction  Sutton and Barto: Ch 13  
24  4/10  Wed  Reinforcement learning  Learning optimal policies: Dynamic Programming, Monte Carlo; TD learning  Sutton and Barto: Ch 4, 5, 6 

25  4/15  Mon  Learning Theory  Learning theory overview; VC dimension; Generalization Bound  http://cs229.stanford.edu/notes/cs229notes4.pdf  
26  4/17  Wed  Ensemble Methods  Boosting  Bishop: Ch 14.3 

 



 
4/25  Thur  Final project presentation (time and place: TBD) 

 Final project report due: 4/29 23:59pm 
* NOTE: Attendance is optional.
No  Date 
 Review session  Topics  Readings and useful Links  Handouts 
1  TBD 
 Linear Algebra review  Overview of linear algebra, matrix operations and calculus; MATLAB tutorial  Stanford CS229 linear algebra review: http://cs229.stanford.edu/section/cs229linalg.pdf 

2  TBD 
 Probability review  Overview of probability  Stanford CS229 probability review: http://cs229.stanford.edu/section/cs229prob.pdf 
