This course is about methodologies and algorithms allowing computer programs to learn from data (examples) and by this become better in solving some given task. The course covers the main principles of machine learning, concentrating on supervised learning algorithms. Acquaintance will be made with the main types of models, such as decision trees, linear models, distance-based models, probabilistic models, ensemble models and artificial neural networks. Practical assignments will be given to build a deeper understanding of the material.
Note that since 2018 the contents of the machine learning course are considerably different from 2017 and all previous years (https://courses.cs.ut.ee/2017/ml/spring)
- Lectures: Tuesday 10:15, Liivi 2-405
- Practice Sessions:
- Group 1: Thursday 10:15 - 12:00 (404, Liis)
- Group 2: Thursday 12:15 - 14:00 (511, Mari-Liis)
- Group 3: Thursday 12:15 - 14:00 (202, Meelis)
- Lecturer: Meelis Kull (email@example.com)
- Teaching Assistants: Mari-Liis Allikivi (firstname.lastname@example.org) and Liis Kolberg (email@example.com)
- Forum and discussions will be held in Piazza. If you didn't get an invite by email, let us know!
Grading and requirements:
The grade is calculated from the total number of points (max 100). The points can be earned as follows:
- Homeworks (36 points): there will be 6 homeworks, each worth 6 points;
- Tests (64 points): there will be 3 tests worth of 20, 20 and 24 points.
In order to pass the course the student must get at least 50% from homeworks (threshold 18 points) and 50% from each of the tests.
The course will be largely based on the book
"Machine learning: The Art and Science of Algorithms that Make Sense of Data" by Peter Flach (2012)
The language for this course is Python. All practice sessions, examples and support is given only for that language. You are allowed to use another language like R if you wish, but in that case you have to be able to work with it without our help.