Machine Learning

Course Number: LIX004M5
Instructor: Jörg Tiedemann, Alfa-informatica, j.tiedemann@let.rug.nl

Announcements Autumn 2008

2008-08-05: web site is on-line
2008-09-01: room for lectures is A901 (in the building next to the Academiegebouw)

Schedule

Sept 1 - Oct. 24
Lectures: A901, Mon. 09:15-11:00
Labs: Fri. 9:15-11:00 in Harmonie.12.107.
Exam: Friday, 24 Oct. 09:00 - 12:00 in the Academiegebouw AZERN

Prerequisites:

The course is open to students in Computer Science, Artificial Intelligence and Information Science. Required background consists of programming ability, elementary statistics, status as 2nd year student (or higher) in study program.

Coursework, Grades, Responsibilities

There will be 3 obligatory assignments that have to be passed by the students by handing in 3 short reports according to the deadlines specified on this website. Passing these assignments is a prerequiste for the final exam at the end of the course. There will also be a final assignment which will count as 50% of the grade. This assignment is evaluated by means of a written report and an oral presentation during the last lecture of the course. The final written exam counts for the remaining 50% of the grade. Students must accept the responsibilities outlined in the general statement of intellectual responsibility for Information Science students.

Book

Tom Mitchell Machine Learning New York:McGraw-Hill, 1997.

Preliminary program

date	type	topic	literature/info
1 Sep	Lecture	Organization, Introduction	Ch. 1 & 2, slides
5 Sep	Lab 1	evaluation, first assignment, introduction: final projects	Ch. 5, slides, deadline first assignment:8 Sep
8 Sep	Lecture	Decision Trees	Ch. 3, slides
12 Sep	Lab 2	select & get started with final project	mail your selection to Cagri
15 Sep	Lecture	Instance-Based Learning	Ch. 8, slides
19 Sep	Lab 3	second assignment	deadline: 22 Sep
22 Sep	Lecture	Bayesian Learning + EM	Ch. 6, slides
26 Sep	Lab 4	work on final project	e-mail possible questions to Cagri and/or me
29 Sep	Lecture	Sequential data & Markov Models	slides, Manning & Schütze,Ch. 9, Bilmes: What HMMs can do
3 Oct	Lab 5	third assignment	deadline: 6 Oct
6 Oct	Lecture	Reinforcement Learning	Ch. 13, slides
10 Oct	Lab	work on final assignment	deadline report: 3 Nov
13 Oct	Presentations	Student Presentations (two sessions!)	9:15 - 11:00 (A901) AND 11:15 - 13:00 (A2)
24 Oct	Exam		9:00-12:00, AZERN, sample exam (Note: some topics might be not relevant for this year's course)

Guidelines for writing lab reports

All reports must be your own work. No copying.
Write your reports in English. Be assured that you will not lose marks simply for making mistakes in your English
Please submit the reports via e-mail to Cagri
Short reports are marked as passed or not. Group work is possible with not more than 2 students per group. Passing all 3 assignments is required to sign up for the written exam.
The final assignment is marked from 1 to 10. Both, oral presentation and the written report count equally for this mark. A mark larger equal to 6 is required to pass the course.
Give concise but complete answers to the questions.
More detailed guidelines for the final assignment will be given on the website with project proposals.

About WEKA

Some information about WEKA and how to use it (old):

using WEKA
WEKAs file format
interpreting WEKA output
WEKA homepage (documentation, Wiki, ...)