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
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
School: School of Science
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).
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
Machine Learning: Basic Principles
Bayesian Data Analysis
Machine Learning: Advanced Probabilistic Methods
Algorithmic Methods of Data Mining
Kernel Methods in Machine Learning
Research Project in Machine Learning and Data Science
Major optional courses (choose 20-30 credits)
|General optional courses|
|CS-E4840||Information Visualization||5||IV/1st year|
Computational Methods in Stochastics
|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-E5870||High-Throughput Bioinformatics||5||II/2nd year|
|CS-E4880||Machine Learning in Bioinformatics||5||IV-V/1st year (not lectured 2019-2020)|
|CS-E5890||Statistical Genetics and Personalized Medicine||5||IV-V/2nd year (not lectured 2018-2019)|
|Speech and language|
|ELEC-E5500||Speech Processing||5||I/1st year|
Statistical Natural Language Processing
Speech and Language Processing
Also other optional courses may be included per agreement with a professor in charge of the major.