Please note! Course description is confirmed for two academic years (1.8.2018-31.7.2020), which means that in general, e.g. Learning outcomes, assessment methods and key content stays unchanged. However, via course syllabus, it is possible to specify or change the course execution in each realization of the course, such as how the contact sessions are organized, assessment methods weighted or materials used.
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.
Schedule: 08.09.2020 - 18.12.2020
Teacher in charge (valid 01.08.2020-31.07.2022): Juho Rousu
Teacher in charge (applies in this implementation): Juho Rousu
Contact information for the course (applies in this implementation):
CEFR level (applies in this implementation):
Language of instruction and studies (valid 01.08.2020-31.07.2022):
Teaching language: English
Languages of study attainment: English
CONTENT, ASSESSMENT AND WORKLOAD
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
Applies in this implementation:
Assessment Methods and Criteria
Exercises and course exam
Lecture slides and exercises.
- Shalev-Shwartz, Ben-David: Understanding Machine Learning, Cambridge University Press
- Mohri, Rostamizadeh, Talwakar: Foundations of Machine Learning
Substitutes for Courses
Is in overlap with Autumn 2019 instance of Machine Learning: Basic Principles.
CS-C3190 Machine Learning, or MS-C1620 Statistical inference, or equivalent knowledge
Basics of probability theory
Basic linear algebra
SDG: Sustainable Development Goals
9 Industry, Innovation and Infrastructure
- Lärare: Juho Rousu