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Machine Learning, Data Science and Artificial Intelligence (minor)
Basic information
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:
Meeri Heimo
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
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 | |
Previous curricula
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 |
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