KTH
2D1431
Machine Learning
Nada
 

requirements teacher schedule newsgroup labs lectures course pages

Machine Learning

Frank Hoffmann

News:

Course homepage: 2D1431

Course description 2D1431: swedish and english

Start: VT 2002, Period 2:
first lecture: 23-10-2002 13.00 E3

Newsgroup:The course newsgroup nada.kurser.mi serves as a forum for announcements, technical questions, help and exchange of ideas related to the course and the labs. Please check it regularly for new messages.

Schema: schedule

Registration:
Register in RES and subscribe to the course environment with the UNIX commands:
res checkin mi02
course join mi02

For the labs you can reserve an examination (redovisning) time in advance using the RES system with the commands:
module add resultat
bok new mi02
For more information on res and bok use the commands res help and bok help

Teacher:
lecturer: Frank Hoffmann hoffmann@nada.kth.se
I am located at CVAP, here are directions on how to get to CVAP. Please call at 790-6271 in advance to make sure that I am in my office.

lab assistents:

Synopsis:
Machine learning is concerned with computer programs that automatically improve their performance with past experiences. Machine learning draws inspiration from many fields, artificial intelligence, statistics, information theory, biology and control theory.

The objective of this course is to introduce the student to the basic theory and algorithms that form the foundation of machine learning. The course will cover the following topics

Exam:
Exam 14/12/02 solution
Saturday: 14/12/02 8-13, L21-22, L43, L51
Since the course is taught for the first time there are no previous exams. However, to get an idea on what type of questions to expect take a look at the exams in the course 2D1432 Artificial Neural Networks.

Requirements and Grading:
The course is largely self-contained and has no prerequisites other than basic knowledge of computer science and programming experience. To earn credits, a student has to complete the four mandatory lab assignments and to pass a written exam. The labs have to be presented at the specified dates in order to get bonus points. Handing in labs after the scheduled redovisning date results in loss of bonus points to improve your grade.
The grade (betyg) for the course is determined by the outcome of the exam. The maximum number of points in the exam is 40 points, the grades are

Students with a grade 3,4,5 in the exam can improve the grade for the course by presenting lab assignments in time. For each lab presented in time you receive 1.5 bonus points, which means that if you present all four labs in time, you will improve your overall grade by 1. In order to pass the course you have to achieve at least a grade 3 in the exam. Example: a student achieves 25 points in the exam and presents all four labs in time (4x1.5=6 bonus points), the final grade for the course is 4 (25p+6p=31p).

Course literature:
required:

recommended:

Labs:
The course will contain four mandatory labs. Students prepare the solutions to the lab assignments prior to the scheduled lab. During the lab you present your program and answers to the question to the assistent to obtain credits for the lab. Labs can be presented in groups of two, however both students need to fully understand the entire solution and answers. It is also assumed that you complete the assignment on your own and do not use parts of someone else's code or solution. Violation of these rules may result in failing the entire course.

You can reserve a time slot for examination (redovisning) in advance with the command:
bok new mi02
If you need to cancel a reservation use the command:
bok remove mi02

After you presented your lab to the assistent let him sign the lab receipt (labkvitto).

The labs will be programmed in Matlab. It is assumed that you are familiar with the basic features of Matlab. If not you should take a look at the following tutorials and documentation.

The labs will use routines for pattern classification from the Matlab Toolbox for Pattern Recognition at Delft University

Lectures:

Software & Datasets & Links:

for further questions, contact Frank Hoffmann (phone: 790-6271)