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1. Course title

Advanced Course in Machine Learning

2. Course code

-Code in Oodi/OTM (upcoming academic administration information system) and other
systems

DATA12001

3. Course status: compulsory or optional

-Which degree programme is responsible for the course?

Master's Programme in Data Science


-Which module does the course belong to?

Machine learning


-Is the course available to students from other degree programmes?

Yes

4. Course level (first-, second-, third-cycle/EQF levels 6, 7 and 8)

Advanced studies=second-cycle/EQF level 7

Doctoral level = third-cycle (doctoral) degree/EQF level 8

5. Recommended time/stage of studies for completion

First spring

6. Term/teaching period when the course will be offered

Yearly in spring, fourth period

7. Scope of the course in credits

5

8. Teacher coordinating the course

Nikolaj Tatti

9. Course learning outcomes

-Description of the learning outcomes provided to students by the course
- See the competence map (https://flamma.helsinki.fi/content/res/pri/HY350274).

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.

10. Course completion methods

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.

11. Prerequisites

Introduction to Machine Learning or equivalent knowledge

12. Recommended optional studies

-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
course?

Advanced Statistical Inference, Advanced Course in Bayesian Statistics, Data Science Project

13. Course content

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. 


14. Recommended and required literature

-What kind of literature and other materials are read during the course (reading list)?

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.


-Which works are set reading and which are recommended as supplementary reading?

The course planned for 2017 covers Chapters 1-14, 16, 19, 25, 27-18 of the course book.

15. Activities and teaching methods in support of learning

-See the competence map (https://flamma.helsinki.fi/content/res/pri/HY350274).
-Student activities
-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 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. 

16. Assessment practices and criteria, grading scale

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.


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