Helsinki University of Technology → Department of Computer Science and Engineering →
Laboratory of Computer and Information Science → Teaching →
T-61.3050 Machine Learning: Basic Principles → 2007 → Examination

Examination 2007 - T-61.3050

To pass the course you must pass both the examination and the term project.

The examination will contain (about) 6 problems of which you should choose (about) 5. To pass the examination you must achieve about half of the maximum points of the examination. Also see the grading principles of the whole course, as well as guidelines for grading of the examination answers.

The results of the examination are valid for one year after the examination date.

The times and locations of the examinations are given by the examination schelude ("tenttijärjestys"). You must sign in to the examination using WebTOPI at least one week prior to the examination date. Notice that the times and locations of the examinations may change occasionally, check the time and place closer to the examination date.

A programmable calculator, with memory erased, is allowed in the examination. No other extra material (such as text book, lecture slides or solutions to the problems) is allowed. The examination paper is in English and you can answer in Finnish, Swedish or English.

The results of the examination will be announced - after one month has passed from the examination date, at latest - on the (blue binder at the) notice board and by an email to address of the form 12345X@students.hut.fi, where 12345X is your student number.

See the TKK exam regulations (in Finnish) for further information about examinations at TKK.

Reading list

The examination will be based on the topics covered in the lectures.

Please find below a suggested reading list for the examination. In addition to the course text book (Alpaydin 2004), you may find the lecture slides and supplementary materials mentioned in them useful. It is probably also a good idea to go through the problem sets.

The below refer to the chapters of Alpaydin (2004) (see the table of contents):

• Chapter 1
• Chapter 2 (not section 2.5)
• Chapter 3 (not sections 3.8-3.9)
• Chapter 4
• Chapter 5 (not section 5.3)
• Chapter 6 (not sections 6.4-6.5)
• Chapter 7 (not sections 7.5-7.7)
• Chapter 9 (not sections 9.5-9.6)
• Chapter 10 (not sections 10.2, 10.3.2, 10.4, 10.7.2, 10.9)
• Chapter 14 (not sections 14.2.2-14.2.3, 14.6.3, 14.7.2-14.7.4, 14.8) [due to a missing lecture no sections 14.3-14.7 either]
• Appendix A

Additionally, you should know at least the following topics:

• Representation of probabilistic models using Bayesian networks
• Greedy algorithms

References

Alpaydin, 2004. Introduction to Machine Learning. The MIT Press.