Master's Programme in Data Science is responsible for the course.
The course belongs to the Machine learning module.
The course is available to students from other degree programmes.
Recommended time/stage of studies for completion: first spring
Term/teaching period when the course will be offered: yearly in spring, fourth period
Obtains deeper knowledge of domain skills in machine learning: Can describe the basic formulation of machine learning as minimising the expected risk, and recognises alternative formulations for the risk. Can derive practical loss functions starting from the formal definition, and can describe the relationship between probabilistic models and loss minimisation. Can describe clearly the core tasks of unsupervised and supervised learning, and recognises also more advanced learning setups. Is able to derive and implement in a numerical programming language at least one algorithm suitable for each typical unsupervised learning task: clustering, factor analysis and dimensionality reduction. Can derive and implement in a numerical programming language sparse and regularised linear methods for classification and regression, and can implement some non-linear classification methods such as random forests and support vector machines.
The course is completed via a combination of exam and exercises, and both parts need to be passed to complete the course. Part of the exercises involve programming.
Completing the course with separate exam requires solving a small research project.
|Edeltävät opinnot tai edeltävä osaaminen
Prerequisites in terms of knowledge
Understanding of probability calculus and statistics (including multivariate statistics), linear algebra (matrix calculus) and differential calculus (differentiation and integration). One needs to be able to fluently follow mathematical description of methods and algorithms based on these concepts, as well as to perform simple derivations. Programming skills in some numerical language (typically Python or R) sufficient for implementing machine learning algorithms.
Prerequisites for students in the Data Science programme, in terms of courses
DATA11002 Introduction to Machine Learning
Prerequisites for other students in terms of courses
DATA11002 Introduction to Machine Learning
Recommended preceding courses
MAT22005 Bayesian Inference, DATA20001 Deep Learning, DATA12002 Probabilistic Graphical Models
|Suositeltavat valinnaiset opinnot
Courses in the Machine Learning and Statistical Data Science modules
Other courses that 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 as risk minimisation and as probabilistic modelling. Different kinds of machine learning tasks, covering also advanced setups such as transfer learning. 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.
|Oppimateriaali ja kirjallisuus
Course book: Kevin P. Murphy "Machine Learning: A Probabilistic Perspective", MIT Press, 2012.
The course book is complemented with additional publicly available material, and the course book may change in future.
|Oppimista tukevat aktiviteetit ja opetusmenetelmät
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 they need to solve exercise problems including problems involving programming tasks to reach the learning outcomes related to implementation skills. Some of the exercise problems are formulated in an open manner to support acquisition of problem-solving skills, and require written presentation to facilitate learning of scientific presentation skills.
|Arviointimenetelmät ja -kriteerit
Grading scale is 1...5.
The grading is based on a combination of a course exam and exercises. One should obtain half of the points for both the exam and the exercises to pass the course.
The course can alternatively be taken by completing a separate exam and a project work. One should obtain half of the points for both the exam and the project to pass the course.
Suitable for exchange students