There will be brief project presentations on March 2,4,7,9 in class. Please sign up.

Your project report is due Th. March 10 midnight in a EEE dropbox. Please keep it brief (say ,up to 5 pages). Describe what you did. Make very sure you explain your own contribution and how it is different from what the other ion your group have done. Also make sure you are precise about all input you received, literature referencves etc. Evidence that you submitted to the leaderboard fo some competition is highly desriable. A scientific analysis is also desriable, e.g. report on how well do various methods you tried compare, what are the weaknesses and stengths of the methods etc.

Your HW, quizzes, comprehensive quiz, and project make up your final grade.

Instructor: Max Welling

**Grading & Homwork:**

Every week there will be homework mostly consisting of a coding assignement. Coding will be done in MATLAB. Finishing all your homework assignments is required for passing this class. If you do not hand in your HW before the weekly deadline, you will acculumate penalty points (or viewed more positively, if you hand in your HW in time every week you will accumulate bonus points). I may ask students to demo their implementation of the HW assignment in class.

There will be short quizzes every week, starting in week 2, but no midterm. The decision on a final will be taken later.

Quizzes will be multiple choice, and you will need to buy

There will also be a project, starting right from day 1. Details are given below. You may work in group's of up to 5 students but every member must do their own coding work. Each group will also be required to do a presentation and write a brief report (as a group). It is important that the various contributions of the groupmembers will be made transparent.

Your final grade will be determined as a combination of Homework, Project and Quizzes and perhaps exams.

**Projects:**

You are required to submit your code through a dropbox in EEE and write a brief report with your group.

You must show evidence that you participated in the Kaggle competition (hopefully by winning the cash award :-) )

You may work in groups no larger than 5 people

More details will be explained in class.

This project may not simply be whatever you were doing anyway for your PhD research.

Please submit a proposal for my review

-Decision Theory, Loss functions, ROC curves [ppt]pdf]

-Regression, Bias-Variance Tradeoff [ppt] [pdf]

-PCA & Kernel PCA [ppt] [pdf]

-Ridge Regression & Kernel RG (classnotes)

-Clustering [ppt] [pdf]

-DT [ppt] [pdf]

-Spectral Clustering (classnotes)

-Ensemble Methods (classnotes)

-Boosting [ppt] [pdf]

-FisherDiscriminant Analysis (classnotes)

-Canonical Correlation Analysis & Maximum Autocorrelation Factor Analysis (classnotes)

-Convex OPtimization (classnotes)

-SVMs [ppt] [pdf]

-Classifier Evaluation [ppt][pdf] (probably just for your own interest)

** **** **

Week 2: Homework 2 [doc] [pdf]

Week 3: Homework 3 [doc] [pdf]

Week 4: Homework 4 [doc] [pdf]

Week 5: Homework 5 [doc] [pdf]

Week 6: Homework 6 [doc][pdf]

Week 7: Homework 7 [doc] [pdf]

Week 8: Homework 8 [doc][pdf]

Week 9: Homework 9 [doc] [pdf]

Week 10: Homework 10 [doc][pdf] (probably just for your own interest)

The textbook that will be used for this course is:

Optional side readings are:

3. R.O. Duda, P.E. Hart, D. Stork: Pattern Classification

4. C.M. Bishop: Neural Networks for Pattern Recognition

5. T. Hastie, R. Tibshirani, J.H, Friedman: The Elements of Statistical Learning

6. B.D. Ripley: Pattern Recognition and Neural Networks

7. T. Mitchell: Machine Learning.