Advanced Course in Machine Learning
-Code in Oodi/OTM (upcoming academic administration information system) and other
-Which degree programme is responsible for the course?
Master's Programme in Data Science
-Which module does the course belong to?
-Is the course available to students from other degree programmes?
Advanced studies=second-cycle/EQF level 7
Doctoral level = third-cycle (doctoral) degree/EQF level 8
Yearly in spring, fourth period
The learning outcomes provided to students by the course:
The course is completed by completing a set of exercises. Significant part of the exercises involve programming.
Introduction to Machine Learning or equivalent knowledge. Programming skills, especially with python, are highly recommended. Basic knowledge of linear algebra, statistics, and analysis, especially differentiation, is required.
-What other courses are recommended to be taken in addition to this course?
Courses in the Machine Learning and Statistical Data Science modules
-Which other courses support the further development of the competence provided by this
Advanced Statistical Inference, Advanced Course in Bayesian Statistics, Data Science Project
Formulation of machine learning problems. Different kinds of machine learning tasks. Common optimisation approaches for machine learning. Unsupervised learning methods: clustering, factor analysis, matrix factorisation, non-linear dimensionality reduction. Supervised learning methods: Linear and non-linear classifiers, kernel methods, decision trees and forests, boosting.
14. Recommended and required literature
-What kind of literature and other materials are read during the course (reading list)?
The course material are slides, supported by the course book:
Kevin P. Murphy "Machine Learning: A Probabilistic Perspective", MIT Press, 2012.
The material is complemented with additional publicly available material, and the course book may change in future.
-See the competence map (https://flamma.helsinki.fi/content/res/pri/HY350274).
-Description of how the teacher’s activities are documented
The primary mode of instruction consists of lectures and exercise sessions with active guidance, supported by other forms of teaching methods when applicable. The students are encouraged to attend the lectures and the exercise sessions. Support channel (online chat) is provided for students.
Grading scale is 1...5.
The grading is based on the points received from the exercises.