Machine Learning and Data Mining

Professor in charge: Samuel Kaski
Other professors: Aristides Gionis, Alex Jung, Juha Karhunen, Jouko Lampinen, Harri Lähdesmäki, Heikki Mannila, Juho Rousu, Aki
Vehtari
Extent: Long major (55-65 credits). Compact major is not offered. Students who want to take a minor are encouraged to include it in elective studies.
Abbreviation: Macadamia
Code: SCI3044
School: School of Science

Objectives

The major in Machine Learning and Data Mining (Macadamia) gives a strong basic understanding of modern computational data analysis and modelling methodologies. It builds on the strong research at the Department of Computer Science. The methods of machine learning and data mining are applicable and needed in a wide variety of fields ranging from process industry to mobile communications, social networks and artificial intelligence. Recent spearhead application areas include bioinformatics, computational linguistics, multimodal interfaces, and intelligent information access.

The major provides an excellent basis for doctoral studies as well as industrial research and development work. Teaching and supervision for Macadamia students is given by an enthusiastic and experienced group headed by world leaders in this research field. Excellent Macadamia students can continue their studies in the Helsinki Doctoral Education Network in Information and Communication Technology (HICT).

Learning Outcomes

1) The student is able to formalize data analysis problems in terms of the underlying statistical and computational principles

2) The student is able to assess suitability of different machine learning methods for solving a particular new problem encountered in industry or academia, and apply the methods to the problem.

3) The student can interpret the results of a machine learning algorithm, assess their credibility, and communicate the results with experts of other fields.

4) The student can implement common machine learning methods, and design and implement novel algorithms by modifying the existing approaches.

5) The student understands the theoretical foundations of the machine learning field to the extent required for being able to follow research in the field

Content and Structure

The students have to take the eight compulsory courses. In addition, they include courses from the major optional courses list. Also other optional courses may be included per agreement with a professor in charge of the major.

Major compulsory courses 40 credits

 CODE

NAME

CREDITS

PERIOD/YEAR

CS-E3210

Machine Learning: Basic Principles

5

I-II/1st year

CS-E5710

Bayesian Data Analysis

5

I-II/1st year

CS-E4890

Deep Learning

5

II/1st year

CS-E4820

Machine Learning: Advanced Probabilistic Methods

5

III-IV/1st year

CS-E4600

Algorithmic Methods of Data Mining

5

I/1st year

CS-E4830

Kernel Methods in Machine Learning

5

I-II/2nd year

CS-E4840

Information Visualization

5

IV/1st year

CS-E4870

Research Project in Machine Learning and Data Science

5-10

varies/2nd year

Major optional courses (choose 15-25 credits)

CODE

NAME

CREDITS

PERIOD/YEAR

CS-E5790

Computational Science

5

I-II/1st year

CS-E4850

Computer Vision

5

I-II/2nd year

ELEC-E5510 

Speech Recognition

5

II/2nd year

ELEC-E5550

Statistical Natural Language Processing

5

III-IV/1st year

CS-E5870

High-Throughput Bioinformatics

5

II/2nd year

CS-E4800

Artificial Intelligence

5

III-IV/1st year

CS-E4004

Individual Studies in Computer Science

1-10

Agreed with the teacher

CS-E4070

Special Course in Machine Learning and Data Science

3-10

varies

CS-E4880

Machine Learning in Bioinformatics

3-10

I-II

CS-E5890

Statistical Genetics and Personalized Medicine

5 IV-V

Also other optional courses may be included per agreement with a professor in charge of the major. 

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