You can find course descriptions in Sisu. In your study plan, choose the course and click the course code or search courses by code or name. Learning environments are found in MyCourses through search or after registration in "My own courses".
Machine Learning, Data Science and Artificial Intelligence (minor)
Basic information
Code:
SCI3070
Extent:
20–25
ECTS
Curriculum:
2026–2028
2024–2026
2022–2024
2020–2022
Previous curricula
Level:
Advanced studies
Language of learning:
English
Target group:
All Aalto students
Teacher in charge:
Pekka Marttinen
Administrative contact:
Anu Kuusela
Organising department:
Department of Computer Science
Organiser:
School of Science
Prerequisites:
Bachelor-level minor in Computer Science or equivalent knowledge. Please check the prerequisites of the courses before signing up.
Quotas and restrictions:
No quotas.
Application process:
No application process. Open for all students of Aalto University.
Content and structure of the minor
Students take the compulsory course CS-C3240 Machine Learning and select additional courses from the optional courses list.
| Code | Course name | ECTS | Teaching |
|---|---|---|---|
Compulsory course 5 ECTS |
|||
| CS-C3240 | Machine Learning * | 5 | I |
Optional courses 15-20 ECTS |
|||
| CS-E4715 | Supervised Machine Learning | 5 | I-II |
| CS-E5710 | Bayesian Data Analysis | 5 | I-II |
| CS-E4650 | Methods of Data Mining | 5 | I-II |
| CS-E4800 | Artificial Intelligence | 5 | III-IV |
| CS-E4890 | Deep Learning | 5 | III-IV |
| CS-E5795 | Computational Methods in Stochastics | 5 | I-II |
| CS-E4825 | Probabilistic Machine Learning | 5 | III-IV |
| CS-E4740 | Federated Learning | 5 | IV-V |
| CS-E4895 | Gaussian Processes | 5 | IV-V |
| CS-E4850 | Computer Vision | 5 | I-II |
| ELEC-E8125 | Reinforcement Learning | 5 | I-II |
| CS-E4891 | Deep Generative Models | 5 | IV-V |
| ELEC-E5550 | Statistical Natural Language Processing | 5 | III-IV |
* If the course has been taken as part of the B.Sc. studies, it can be substituted with any of the minor's optional courses.
Previous curricula
Code: SCI3070
Extent: 20–25 ECTS
Curriculum: 2024–2026 2022–2024 2020–2022 Previous curricula
Level: Advanced studies
Language of learning: English
Theme: ICT and digitalisation
Target group: All Aalto students
Teacher in charge: Pekka Marttinen
Administrative contact: Anu Kuusela
Organising department: Department of Computer Science
School: School of Science
Prerequisites: Bachelor-level minor in Computer Science or equivalent knowledge. Please check the prerequisites of the courses before signing up.
Quotas and restrictions: -
Application process: -
Content and structure of the minor
You can find course descriptions in Sisu. In your study plan, choose the course and click the course code or search courses by code or name. Learning environments are found in MyCourses through search or after registration in "My own courses".
Students take the compulsory course CS-C3240 Machine Learning and select additional courses from the optional courses list.
| Code | Course name | ECTS | Period |
|---|---|---|---|
Compulsory course 5 ECTS |
|||
| CS-C3240 | Machine Learning | 5 | |
Optional courses 15-20 ECTS |
|||
| CS-E4715 | Supervised Machine learning | 5 | |
| CS-E5710 | Bayesian Data Analysis | 5 | |
| CS-E4650 | Methods of Data Mining | 5 | |
| CS-E4800 | Artificial Intelligence | 5 | |
| CS-E4890 | Deep Learning | 5 | |
| CS-E5795 | Computational Methods in Stochastics | 5 | |
| CS-E4825 | Probabilistic Machine Learning | 5 | |
| CS-E4740 | Federated Learning | 5 | |
| CS-E4895 | Gaussian Processes | 5 | |
| CS-E4450 | Explorative Information Visualization | 5 | |
| CS-E4850 | Computer Vision | 5 | |
| CS-E4680 | Quantum Machine Learning | 5 | |
| ELEC-E8125 | Reinforcement Learning | 5 | |
| CS-E4891 | Deep Generative Models | 5 | |
| ELEC-E5550 | Statistical Natural Language Processing | 5 | |
Code: SCI3070
Extent: 20–25 ECTS
Language of instruction: English
Level: Masters
Target group: All Aalto students
Theme: ICT and digitalisation
Teacher in charge: Pekka Marttinen
Administrative contact: Anu Kuusela
Organising department: Department of Computer Science
Prerequisites: Bachelor-level minor in Computer Science or equivalent knowledge. Please check the prerequisites of the courses before signing up.
Quotas and restrictions: No quotas
Application process: Open for all students of Aalto University
Content and structure of the minor
Students take the compulsory course CS-C3240 Machine Learning and select additional courses from the optional courses list.
Structure of the minor
| Code | Course name | ECTS credits |
|---|---|---|
| Compulsory course | 5 | |
| CS-C3240 | Machine Learning D | 5 |
| Optional courses | 15–20 | |
| CS-E4710 | Machine Learning: Supervised Methods D | 5 |
| CS-E5710 | Bayesian Data Analysis D | 5 |
| CS-E4650 | Methods of Data Mining D | 5 |
| CS-E4800 | Artificial Intelligence D | 5 |
| CS-E4890 | Deep Learning D | 5 |
| CS-E5795 | Computational Methods in Stochastics D | 5 |
| CS-E4820 | Machine Learning: Advanced Probabilistic Methods D | 5 |
| CS-E4740 | Federated Learning D | 5 |
| CS-E4830 | Kernel Methods in Machine Learning D NO TEACHING 2023-2024! | 5 |
| CS-E4895 | Gaussian Processes D | 5 |
| CS-E4450 | Explorative Information Visualization D | 5 |
| CS-E4850 | Computer Vision D | 5 |
| CS-E4680 | Quantum Machine Learning D | 5 |
| ELEC-E8125 | Reinforcement Learning D | 5 |
Code: SCI3070
Extent: 20-25 credits
Language: English
Teacher in charge: Senior University Lecturer Jorma Laaksonen
Administrative contact: Anu Kuusela
Target group: All master's students with sufficient prerequisite knowledge.
Application procedure: Open for all students of Aalto University
Quotas and restrictions: No quotas
Prerequisites: Bachelor-level minor in Computer Science or equivalent knowledge. Please check the prerequisites of the courses before signing up.
Content and structure of the minor
Students take the compulsory course CS-C3240 Machine Learning and select additional courses from the optional courses list.
Structure of the minor
| Code | Course name | ECTS credits |
|---|---|---|
| Compulsory course | 5 | |
| CS-C3240 | Machine Learning | 5 |
| Optional courses | 15-20 | |
| CS-E4710 | Machine Learning: Supervised Methods | 5 |
| CS-E5710 | Bayesian Data Analysis | 5 |
| CS-E4800 | Artificial Intelligence | 5 |
| CS-E4890 | Deep Learning | 5 |
| CS-E4820 | Machine Learning: Advanced Probabilistic Methods | 5 |
| CS-E4650 | Methods of Data Mining | 5 |
| CS-E4830 | Kernel Methods in Machine Learning | 5 |
| CS-E4840 | Information Visualization | 5 |
| CS-E4875 | Research Project in Machine Learning, Data Science and Artificial Intelligence | 5 |
Code: SCI3070
Extent: 20-25 credits
Language: English
Teacher in charge: Professor Samuel Kaski
Administrative contact: Anu Kuusela
Target group: All master's students with sufficient prerequisite knowledge.
Application procedure: Open for all students of Aalto University
Quotas and restrictions: No quotas
Prerequisites: Bachelor-level minor in Computer Science or equivalent knowledge. Please check the prerequisites of the courses before signing up.
Content and structure of the minor
Students take the compulsory course CS-E3210 Machine Learning: Basic Principles and select additional courses from the elective courses list.
Structure of the minor
| Code | Course name | ECTS credits |
|---|---|---|
| Compulsory course | 5 | |
| CS-E3210 | Machine Learning: Basic Principles | 5 |
| Elective courses | 15-20 | |
| CS-E5710 | Bayesian Data Analysis | 5 |
| CS-E4890 | Deep Learning | 5 |
| CS-E4820 | Machine Learning: Advanced Probabilistic Methods | 5 |
| CS-E4600 | Algorithmic Methods of Data Mining | 5 |
| CS-E4830 | Kernel Methods in Machine Learning | 5 |
| CS-E4840 | Information Visualization | 5 |
| CS-E4870 | Research Project in Machine Learning and Data Science | 5 |
Code: SCI3070
Extent: 20-25 credits
Language: English
Teacher in charge: Professor Samuel Kaski
Target group: All master's students with sufficient prerequisite knowledge.
Application procedure: Open for all students of Aalto University
Quotas and restrictions: No quotas
Prerequisites: Bachelor-level minor in Computer Science or equivalent knowledge. Please check the prerequisites of the courses before signing up.
Content and structure of the minor
Students take the compulsory course CS-E3210 Machine Learning: Basic Principles and select additional courses from the elective courses list.
Structure of the minor
| Code | Course name | ECTS credits |
|---|---|---|
| Compulsory course | 5 | |
| CS-E3210 | Machine Learning: Basic Principles | 5 |
| Elective courses | 15-20 | |
| CS-E5710 | Bayesian Data Analysis | 5 |
| CS-E4890 | Deep Learning | 5 |
| CS-E4820 | Machine Learning: Advanced Probabilistic Methods | 5 |
| CS-E4600 | Algorithmic Methods of Data Mining | 5 |
| CS-E4830 | Kernel Methods in Machine Learning | 5 |
| CS-E4840 | Information Visualization | 5 |
| CS-E4870 | Research Project in Machine Learning and Data Science | 5 |