Hard copy is required for all assignments. If you are not on campus for the start of class (what?!?!) and want to email your assignment to me or the TA to establish that it was done on time, that's OK. However, you must hand in hard copy before the assignment will be graded.
This class is open to both advanced undergraduate students and graduate students. The homeworks and exams will contain some questions to be answered by all students (those enrolled in either 478 or 678), and one or more questions to be answered just by those students enrolled in 678.
The project is meant to give students deeper exposure to some topic in machine learning than they would get from the lectures, readings, and discussions alone. Those projects that are most successful often blend the student's own research with machine learning, e.g. by applying machine learning techniques to a problem in some other area, or by bringing an insight from some other area to a problem in machine learning. However, projects need not involve ties with ongoing research. Many good projects in the past have investigated the application of existing algorithms to a domain/dataset of interest to the student, such as Texas Hold'em, the stock market, sporting events, and so on. Students can come up with their own project ideas or they can come see me and we'll brainstorm project ideas. Note that the project accounts for a significant fraction of the final grade, so there will be milestones throughout the semester to ensure that everyone is making good progress. See the syllabus below for more information.
Projects may be done by individuals or teams of two people. However, teams of two will be expected to do significantly more work than what is expected of an individual project. More information on projects, along with potential project ideas, can be found here.
For this course in particular it is both OK and a good idea for students to study together, including discussing homework problems. However, whatever a student turns in must be his/her own. A good rule of thumb is that it is OK to talk about problems, but it is not OK to share written materials or code. If you incorporate written materials or code from any source in the project, an appropriate citation is required.




1  Wed 26 Jan  Course overview  supervised learning, hypothesis spaces  Read Bishop Chapter 1 through the end of section 1.1 
2  Mon 31 Jan  Decision trees  information gain, overfitting, pruning  Decision tree slides
(Thanks to Tom Mitchell) Book chapter on deicision trees Bishop chapter 1 slides (Thanks to Chris Bishop) 
3  Wed 2 Feb  Decision trees, nearest neighbor  Nearest neighbor slides (Thanks to Andrew Moore) 
4  Mon 7 Feb  Nearest neighbor  
5  Wed 9 Feb  Review of probability and statistics  See slides from class #2, Bishop chapter 1 Homework 1 assigned 
6  Mon 14 Feb  Bayesian classifiers, naive Bayes  Naive Bayes slides (Thanks
to Tom Mitchell)
Naive Bayes readings (Sections 1 and 2 of the chapter from Tom Mitchell) 
7  Wed 16 Feb  Bayesian classifiers continued  
8  Mon 21 Feb  Perceptrons, logistic regression  Perceptron, logistic, LDA, and LR
slides (Thanks to Tom Dietterich) Homework 1 due 
9  Wed 23 Feb  Linear discriminant analysis, linear regression  Homework 2 assigned Slides (1  32) (Thanks to Andrew Moore) 
10  Mon 28 Feb  Linear regression and basis functions  Bishop section 3.1 
11  Wed 2 Mar  Bias/variance theory  Slides (Thanks to Tom Dietterich) Bishop section 3.2 
12  Mon 7 Mar  Bias/variance theory, ensemble methods  bagging, boosting  Homework 2 due 
13  Wed 9 Mar  Ensemble methods  
14  Mon 14 Mar  Midterm exam  
15  Wed 16 Mar  Overfitting  Slides (Thanks to Andrew Moore) 
Mon 21 Mar  Spring break  
Wed 23 Mar  Spring break  
16  Mon 28 Mar  Experiment design  cross validation, leave one out  Slides, thanks to Tom Dietterich Project proposal due 
17  Wed 30 Mar  Support vector machines  large margin classifiers, the kernel trick  Homework 3 assigned Tutorial paper on SVMs Slides thanks to Andrew Moore 
18  Mon 4 Apr  Support vector machines continued  Section 7.1 in Bishop book 
19  Wed 6 Apr  Bayesian networks  representation, conditional independence  Slides thanks to Andrew Moore Sections 8.1 and 8.2 in Bishop book 
20  Mon 11 Apr  Bayesian networks  inference, variable elimination  Homework 3 due 
21  Wed 13 Apr  Neural networks  
22  Mon 18 Apr  Neural networks  Project midway report due 
23  Wed 20 Apr  Clustering  agglomerative, divisive, kmeans  Homework 4 assigned ionosphere.data, ionosphere.names 
24  Mon 25 Apr  Clustering  
25  Wed 27 Apr  Reinforcement learning  Markov decision processes, value iteration  Readings: chapters 3, 4, and 6 from the Sutton and Barto RL Book Slides for the RL problem; dynamic programming; temporal difference learning 
26  Mon 2 May  Reinforcement learning  policy iteration, Qlearning  
27  Wed 4 May  Project presentations  Homework 4 due 
28  Mon 9 May  Project presentations  
29  Wed 11 May  Project presentations  
Fri 20 May  Final Exam 1:00  3:00PM  Final project writeup due Final exam topics 