Introduction to Machine Learning : Examinations
These instructions apply to all separate examinations based on the Autumn 2013 lecture course. That means separate examinations from Spring 2014 until the next time the course is lectured.
Who can participate?
If you participated in the Autumn 2013 course and completed at least 50% of the homework, but did not pass the exam or wish to improve your grade, you can take the separate examination as a renewal examination. This means that the separate examination replaces the course examination. You don't need to do anything extra in addition to the exam. You homework from Autumn 2013 carries over and will constitute 40% of your grade, as it did in conjunction with the regular course exam.
If you did not complete at least 50% of homework in Autumn 2013, you can take the separate examination only if you complete additionally a set of programming assignements., The details are given below. If you take this route, the programming assignment will constitute 20% of your grade, and the exam 80%.
Programming assingnments for separate examination
You should send your solution to the lecturer (Jyrki Kivinen) as explained in the problem description at least one week before the separete examination you plan to take.
- programming assignment
- MNIST data set
- Newsgroup data set
- Movielens data set
- code for Jaccard coefficient: Matlab, R
What to bring?
As with all the exams at the department, you should bring writing materials (pencil etc. but not your own paper) and some means of identification (student card, passport etc.).
Additionally, to the exams of this course, you may (and probably should) bring
- a pocket calculator (not a computer, tablet, smart phone etc.)
- a "cheat sheet" which is one hand-written A4 sheet, to which you can write whatever information you think might be useful in the exam, using both sides if you wish.
Actually I (the lecturer) can't really think what one would want to write on a cheat sheet of this course. In the (unlikely) case that you need some complicated formulas that are not easy to derive (say, the exact density function of the Gaussian distribution, including normalisation factor), they will be provided. However, preparing a cheat sheet might in itself be useful in clarifying to yourself what is important in the course, even if you end up not needing the sheet in the actual exam.
What will be in the exam?
In the exam, you may be asked to
- briefly define and explain key concepts and terms
- explain algorithms, techniques and other broader topics, possibly answering "what," "why" and "how" questions
- simulate an algorithm on a (very small) data set
- make basic mathematical calculations and derivations
- something else relevant to the content and learning objectives of the course.
Below are some sample exams from previous years. They should give a good idea of what to expect.