DIT380/TDA231: Algorithms for Machine Learning and Inference

2016 Course: http://www.cse.chalmers.se/research/lab/courses/algorithms-for-machine-learning-tda-231/

• Supervised Learning : Bayes Classifier, Fisher Discriminant, Perceptron, Support Vector machines,Regression, Boosting.
• Unsupervised Learning : Clustering Algorithms, EM algorithm, Mixture models, Kernel methods.
• Graphical Models : Graphical models, Hidden Markov models, Belief propagation

Messages

• The final exam is available here

• The final exam will be available on Saturday at 10.00 am. It has to be handed back by Monday 10.00 am at Vinay's office (EDIT6453)

• The deadline for homework 4 has been extended to March 16, 2012

• The following formula will be used to compute the final grades based on grades in assignment and exam:
  • score_1 = 6/4 * assignment_score + exam_score;
  • score_2 = assignment_score + 8/6 * exam_score;
  • final_score = max (score_1, score_2);

• Special guest lecture by Dr. Henk Wymeersch on Belief propagation on Friday (February 24, 2012). More details here.

• Special guest lecture by Dr. Henk Wymeersch on object tracking based on regression using Gaussian processes on Friday (February 10, 2012). More details here.

• Please indicate your choice for exam date at http://www.doodle.com/ef9muf43xmfpazh2.

• There is a correction in Question 2 part (b) in Assignment 1. The updated file can be downloaded here.

• Please join the google group ml_chalmers. The TA's will respond to questions about the assignment on this group.

• Although most of the contents are finalized. However, there might be some changes before the start of course.

• Student representatives: To be announced.

Subscribe to ml_chalmers

Email:

Visit this group

Practical Information

Instructors:

• Chiranjib Bhattacharyya (chibha, room 6452)

• Devdatt Dubhashi (dubhashi, room 6479)

Assistants:

• Muhammad Azam Sheikh (azams, room 6453)

• Bassel Mannaa (bassel, room ----)

• Vinay Jethava (jethava, room 6453)

• Tuesday, 13:15-15:00, room HC2

• Friday, 10:00-11:45, room HC1

Consultation time: To be announced.

Possible changes in the schedule will be announced.

Examination and Grading Criteria

Rules and Policies

• Deadlines are firm. Delays must be motivated before the deadlines. Unannounced late submissions will not be considered. It is everyone's responsibility to observe the deadlines.
• It is allowed, even encouraged, to discuss the exercises during the course. Also, do not hesitate to ask if you have difficulties with the exercises, or if something is unclear.
• You must implement your own code and submit your own results.
• Submitting others' work in your own name is cheating! It can lead to severe consequences, in very bad cases even suspension from studies.
• Specifically, it is prohibited to submit solutions that you got from other persons, unless you explicitly quote the sources and add your own explanations. And: a copy with modifications is, in this sense, still a copy.
• You are also responsible for not giving others the opportunity to copy from your work. (We will not investigate who copied from whom.)

Contents

Weekly Programming Assignments Submission

A submission should contain the course code and exercise number. Also write your name and personal number.

As course goes on, we will add here assignment description and deadlines etc.

Homework	Due Date	Data	Notes
Homework 1	January 27, 2012	dataset1.mat dataset2.mat digits.mat
Homework 2	February 7, 2012	dataset3.mat dataset4.mat
Homework 3	February 17, 2012	d1.mat d2.mat d3.mat
Homework 4	March 16, 2012	q1.mat q2.mat q3.mat data_henk.mat

Literature

• D. Barber, Bayesian Reasoning and Machine Learning , Cambridge University Press 2012. This book is available for free here.
• C. Bishop: Pattern Recognition and Machine Learning. (See here.)
• R. Duda, P. Hart, D. Stork: Pattern Recognition (2nd Edition). (See here.)