Code: SCI3044

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.

Responsible Professor: Samuel Kaski

Other professors:
SCI: Aristides Gionis, Alex Jung, Jouko Lampinen, Harri Lähdesmäki, Heikki Mannila, Juho Rousu, Aki Vehtari, Juho Kannala, Rohit Babbar, Alexander Ilin, Arno Solin, Pekka Marttinen
ELEC: Paavo Alku, Mikko Kurimo, Tom Bäckström

Abbreviation: Macadamia

School: School of Science

Objectives

The major in Machine Learning, Data Science and Artificial Intelligence (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-intensive problems in data science and artificial intelligence 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.

6) The student understands the opportunities that machine learning offers in data science and artificial intelligence.

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 35 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

IV-V/1st year

CS-E4820

Machine Learning: Advanced Probabilistic Methods

5

III-IV/1st year

CS-E4600

Algorithmic Methods of Data Mining

5

I-II/1st year

CS-E4830

Kernel Methods in Machine Learning

5

III-IV/1st year

CS-E4870

Research Project in Machine Learning and Data Science

5–10

varies/2nd year

Major optional courses (choose 20-30 credits)

CODE

NAME

CREDITS

PERIOD/YEAR

General optional courses


CS-E4840Information Visualization5IV/1st year
CS-E5795

Computational Methods in Stochastics

5I-II/1st year

CS-E4850

Computer Vision

5

I-II/2nd year

CS-E4800Artificial Intelligence5III-IV/1st year
CS-E4004Individual Studies in Computer Science1–10agreed with the teacher
CS-E4070Special Course in Machine Learning and Data Science3–10varies
Bioinformatics


CS-E5870High-Throughput Bioinformatics5II/2nd year
CS-E4880Machine Learning in Bioinformatics5IV-V/1st year (not lectured 2019-2020)
CS-E5890Statistical Genetics and Personalized Medicine5IV-V/2nd year (not lectured 2018-2019)
Speech and language


ELEC-E5500Speech Processing5I/1st year

ELEC-E5510 

Speech Recognition

5

II/2nd year

ELEC-E5550

Statistical Natural Language Processing

5

III-IV/1st year

ELEC-E5521

Speech and Language Processing
Methods

5III-IV/1st year

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