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This course has two parts. The first part includes an introduction to the basic machine learning concepts and algorithms,
which will also provide the basis for the second part of the course. The second part covers selected recent topics in machine learning. In particular,
the course will cover the following main topics:
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Part 1: |
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- Concept learning
- Decision trees
- Artificial neural networks
- Evaluating hypotheses
- Bayesian learning
- Instance based learning
- Genetic algorithms
- Reinforcement learning
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Part 2: |
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- Evaluation methods for machine learning
- Cost-sensitive learning
- Ensemble learning
- Learning with multiple sources
- Active learning
- Transfer learning
- Kernel methods
- Learning with graphs
- Structured output learning
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Homework (30%)
Paper presentation (25%)
Paper discussion (10%)
Project (35%)
Homework assignments will be posted on this web site. Assignments will
have some programming and non-programming parts and students are expected to work individually for the assignments.
After the submission, there will be a Q&A session separately held for each student. Students are supposed to be ready
at their session to answer the questions regarding their homework and the related course topics.
Assignments are expected to be turned in at class time on the due date. For the late assignments,
each student will be given a total of three grace days (whole or partial) for the whole semester. Once these late days have been
exhausted, no late assignments will be accepted. As an example, if Student A submits his 1st assignment 29 hours late, he will
have used two late days and have only one day left. If Student A then submits his 2nd assignment 5 hours late, he will have
used his remaining late day. If Student A submits his 3rd assignment 1 minute late, this assignment will not be accepted.
In the second part of the course, we will read and discuss research papers on the selected recent topics of
machine learning. In this part, each student will present a journal paper (or two shorter ones) in the class. The papers are selected
and assigned to the students by the instructor. However, we will try to match the topics with students' interest. Therefore, you are
asked to email your preferences on the topics, which are listed as the topics of the second part, to the instructor by October 5th, 2010,
if you have any preferences. If there are some topics for which several students show interest, the related topics are assigned
to the students on first-come first-serve basis. If there are some topics for which no one shows interest, the related papers are assigned
to the remaining students. Thus, you are advised to give more than one preference. Additionally, you are also welcome to make
suggestions for paper selection. However, there is no guarantee that
your suggested paper is selected and assigned to you. Please email your suggestions to the instructor, if you have any.
Students will have 25-30 minutes to present their paper(s). We will have a discussion period of 10-15 minutes
after the presentation. Students are supposed to submit their presentations to the instructor and these presentations will be graded.
Besides, all students are expected to read the papers presented by the other students and to participate the discussion session.
The participation of the students will also be graded.
As a presenter, each student is expected to read the paper entirely, deeply understand the paper, and prepare a
presentation that clearly explains the paper. The quality of the presentation is also important. As a participant of a discussion session,
each student is expected to ask relevant questions to the presenter and to answer the questions related to the paper. For that, it is
important for you to read and understand the paper entirely before coming to the presentation.
Students will individually conduct a research project. Your project should contain
a relevant part from the topics, which are listed as the topics of the second part of the course. Additionally,
you are required to use proper techniques to evaluate your work (you may want to use some techniques from what we will mention
under the topic of "Evaluation Methods for Machine Learning").
You are required to write a proposal, a progress report, and a final report. Your proposal should provide a short
description of the problem that you want to work on. You are expected to define your own problems.
However, the instructor may approve the proposal or may want to change your problem. Therefore, it is highly recommended to talk to
the instructor on what you want to work on beforehand. Its due date is November 9th, 2010.
The progress report should give the details of the problem and the related literature. It should include the main steps
of the methodology that you use in implementing what you proposed. It should also give the experimental setup and evaluation
techniques that you plan to use. Its due date is December 7th, 2010.
The final report should include the final steps of the methodology, your experiments, their results, and
your interpretations. It should also contain thorough literature search. The report should be written in the format that will later be provided.
Its due date is January 10th, 2010.
This course follows the Bilkent University Code of Academic Integrity, as explained in
the
Student Disciplinary Rules and Regulation. Violations of the rules will not be tolerated.
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