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Signal Processing and Data Science (minor)
Basic information
Code:
ELEC3070
Extent:
20–25
ECTS
Curriculum:
2024–2026
2022–2024
Level:
Advanced studies
Language of learning:
English
Theme:
ICT and digitalisation
Target group:
All Aalto students
Teacher in charge:
Esa Ollila
Administrative contact:
Outi Väisänen
Organising department:
Department of Information and Communications Engineering
School:
School of Electrical Engineering
Prerequisites:
-
Quotas and restrictions:
-
Application process:
No separate application procedure.
Content and structure of the minor
About the minor
Intended learning outcomes
The purpose of the minor is to provide the students with basics of modern Signal Processing and Data Science and the ability to apply those in various fields of science and technology. You'll learn to extract useful information, discover patterns, and make predictions from large amounts of signal or data, especially involving physical sensors and systems, like communication systems or energy distribution networks. You will be able to formalize problems in signal processing in terms of mathematical and statistical models and design and choose computational algorithms to efficiently solve them.
Content
| Code | Course name | ECTS | Period |
|---|---|---|---|
| ELEC-E5410 | Signal Processing for Communications | 5 | I-II / 1 |
| ELEC-E5431 | Large Scale Data Analysis P | 5 | III-IV / 1 |
Choose 10-15 credits from the list below |
|||
| ELEC-E5424 | Convex Optimization D | 5 | I-II |
| CS-E4715 | Supervised Machine Learning | 5 | I-II / 1 |
| ELEC-E5440 | Statistical Signal Processing D | 5 | I-II |
| CS-E4890 | Deep Learning D | 5 | III-IV |
| ELEC-E5620 | Audio Signal Processing D | 5 | II-IV |
| ELEC-E7211 | Digital Wireless Communication D | 5 | I-II |
| ELEC-E5500 | Speech Processing | 5 | I-II |
| ELEC-E5510 | Speech Recognition P | 5 | II |
| ELEC-E8106 | Bayesian Filtering and Smoothing D | 5 | III-IV |
| ELEC-E8740 | Basics of sensor fusion D | 5 | I-II |
| ELEC-E8739 | AI in health technologies D | 5 | I-II |
| ELEC-E5480 | Statistical Machine Learning for Communications and Physical Systems D | 5 | I-II |
| ELEC-E5810 | Biosignal Processing D | 5 | I |
Previous curricula
Code: ELEC3070
Extent: 20–25 ECTS
Curriculum: 2024–2026
Level: Masters
Language of instruction: English
Theme: ICT and digitalisation
Teacher in charge: Esa Ollila
Administrative contact: Eeva Halonen
Organising department: Department of Signal Processing and Acoustics
Target group: All Aalto students
Prerequisites: -
Quotas and restrictions: -
Application process: No separate application procedure.
Objectives
The purpose of the minor is to provide the students with a background in modern Signal Processing and Data Science. You'll learn to extract useful information, discover patterns, and make predictions from large amounts of signal or data, especially involving physical sensors and systems, like communication systems or energy distribution networks. The minor consists of two common compulsory courses (10 cr) and two or three elective courses (10-15 cr).
| Code | Course name | ECTS credits | Period |
|---|---|---|---|
| Compulsory courses (10 credits): | |||
| ELEC-E5410 | Signal Processing for Communications | 5 | I-II / 1 |
| ELEC-E5431 | Large Scale Data Analysis P | 5 | III-IV / 1 |
| Optional courses (choose 10-15 credits): | |||
| ELEC-E5424 | Convex Optimization D | 5 | I-II |
| CS-E4710 | Machine Learning: Supervised Methods | 5 | I-II / 1 |
| ELEC-E5440 | Statistical Signal Processing P | 5 | I-II |
| CS-E4650 | Methods of Data Mining | 5 | I-II |
| CS-E4890 | Deep Learning | 5 | II |
| ELEC-E5620 | Audio Signal Processing | 5 | II-IV |
| CS-E4830 | Kernel Methods in Machine Learning | 5 | I-II |
| ELEC-E7210 | Communication Theory D | 5 | I-II |
| ELEC-E5500 | Speech Processing | 5 | I-II |
| ELEC-E5510 | Speech Recognition P | 5 | II |
| ELEC-E5550 | Statistical Natural Language Processing P | 5 | III-IV |
| ELEC-E8106 | Bayesian Filtering and Smoothing D | 5 | III-IV |
| ELEC-E8740 | Basics of sensor fusion D | 5 | I-II |
| ELEC-E8739 | AI in health technologies D | 5 | I-II |