General
Note: All sessions of the course are online, there are no physical lectures or exercise sessions!
Contents
Generalization error analysis and estimation; Model selection; Optimization and computational complexity; Linear models; Support vector machines and kernel methods; Boosting; Feature selection and sparsity; Multi-layer perceptrons; Multi-class classification; Ranking; Multi-output learning
Course position and Prerequisites
Course is MSc course in Machine learning, targeted to 1st year MSc students in CCIS and Life Science Technologies programmes.
The course assumes basic background in computer science and statistics, as follows:
- CS-C3190 Machine Learning, or MS-C1620 Statistical inference, or equivalent knowledge
- Basics of probability theory
- Basic linear algebra
- Programming skills (Python preferable )
Learning Outcomes
After the course, the student knows how to recognize and formalize supervised machine learning problems, how to implement basic optimization algorithms for supervised learning problems, how to evaluate the performance supervised machine learning models, and has understanding of the statistical and computational limits of supervised machine learning, as well as the principles behind commonly used machine learning models.
Course schedules
Note: All sessions of the course are online, there are no physical lectures or exercise sessions.
- Lectures (online): Tuesdays 10:15-12:00, streamed online and recorded (See the tab Streaming/Recording). Attending the lectures is voluntary.
- Assignments : completed at home, and submitted online (See the tab Assignments).
- Tutorial sessions: Fridays 10:15-12:00. The sessions alternate between
- Q&A sessions (help for solving the exercises). We will organize the Question & Answer sessions as chat sessions, where the submitted questions (to the "General discussion" section) will be answered by text during the already specified schedule. Please remember to submit your questions 24 hours ahead of the session and avoid submitting repetitive questions. Attending the Q&A sessions is voluntary.
- Solution sessions (presenting the solutions for the exercise set). Attending the solution sessions is voluntary.
- Exam (online): 18.12.2020, 13-16. The exam will be open book.
Course personnel
- Lecturer: Prof. Juho Rousu
- Course assistants: Dr Sandor Szedmak, Dr Maryam Sabzevari, Dr Riikka Huusari
Grading
The course can be completed by two alternative ways:
- Exercises (max 30 points) + Exam (max 70 points) , giving a grade 0..5. Lowest passing points total is 50. 85 points will give the grade of 5.
- Exam only (max. 100 points), giving a grade 0...5. 50 points will give the grade 1, 85 points will give the grade of 5.
The better of the resulting two grades will be taken into account.
Language of Instruction
English
Course Material
Lecture slides and exercises are the examined content
Additional reading
The lectures are mostly based on the books:
- Shalev-Shwartz, Ben-David: Understanding Machine Learning, Cambridge University Press. Downloadable for personal use from https://www.cs.huji.ac.il/~shais/UnderstandingMachineLearning/courses.html
- Mohri, Rostamizadeh, Talwakar: Foundations of Machine Learning. Downloadable from https://cs.nyu.edu/~mohri/mlbook/