Search result: Course units in Autumn Semester 2024
Electrical Engineering and Information Technology Master  | ||||||
 Master Studies (Programme Regulations 2018) | ||||||
| Track: Signal Processing and Machine Learning The core courses and specialisation courses below are a selection for students who wish to specialise in the area of "Signal Processing and Machine Learning ", see https://www.ee.ethz.ch/studies/main-master/areas-of-specialisation.html. The individual study plan is subject to the tutor's approval. | ||||||
| Core Courses These core courses are particularly recommended for the field of "Signal Processing and Machine Learning". You may choose core courses form other fields in agreement with your tutor. A minimum of 24 credits must be obtained from core courses during the MSc EEIT. | ||||||
| Foundation Core Courses Fundamentals at bachelor level, for master students who need to strengthen or refresh their background in the area. | ||||||
| Number | Title | Type | ECTS | Hours | Lecturers | |
|---|---|---|---|---|---|---|
| 227-0101-00L | Discrete-Time and Statistical Signal Processing | W | 6 credits | 4G | H.‑A. Loeliger | |
| 227-0105-00L | Introduction to Estimation and Machine Learning  | W | 6 credits | 4G | H.‑A. Loeliger | |
| Advanced Core Courses | ||||||
| Number | Title | Type | ECTS | Hours | Lecturers | |
| 227-0423-00L | Neural Network Theory Does not take place this semester. | W | 4 credits | 2V + 1U | H. Bölcskei | |
| 227-0447-00L | Image Analysis and Computer Vision | W | 6 credits | 3V + 1U | E. Konukoglu, E. Erdil, F. Yu | |
| 252-0535-00L | Advanced Machine Learning | W | 10 credits | 3V + 2U + 4A | J. M. Buhmann, C. Cotrini Jimenez | |
| 263-3210-00L | Deep Learning | W | 8 credits | 3V + 2U + 2A | T. Hofmann | |
| 401-4944-20L | Mathematics of Data Science | W | 8 credits | 3V + 2U | A. Sousa Bandeira | |
| Specialisation Courses These specialisation courses are particularly recommended for the area of "Signal Processing and Machine Learning", but you are free to choose courses from any other field in agreement with your tutor. A minimum of 40 credits must be obtained from specialisation courses during the MSc EEIT. | ||||||
| Number | Title | Type | ECTS | Hours | Lecturers | |
| 227-0116-00L | VLSI 1: HDL Based Design for FPGAs | W | 6 credits | 5G | F. K. Gürkaynak | |
| 227-0121-00L | Communication Systems | W | 6 credits | 4G | C. Studer, S. M. Moser | |
| 227-0155-00L | Machine Learning on Microcontrollers Registration in this class requires the permission of the instructors. Preference is given to students in the MSc EEIT. | W | 6 credits | 4G | M. Magno | |
| 227-0225-00L | Linear System Theory | W | 6 credits | 5G | J. Lygeros, A. Tsiamis | |
| 227-0384-00L | Ultrasound Fundamentals and Applications in Biology and Medicine Previously (up to spring 2020) offered as Ultrasound Fundamentals, Imaging, and Medical Applications | W | 4 credits | 3G | X. L. Dean Ben | |
| 227-0417-00L | Information Theory I | W | 6 credits | 4G | A. Lapidoth | |
| 227-0421-00L | Learning in Deep Artificial and Biological Neuronal Networks | W | 4 credits | 3G | B. Grewe | |
| 227-0477-00L | Acoustics I | W | 3 credits | 2G | R. Pieren | |
| 227-0492-00L | Statistical Learning Theory: on the sample complexity problem | W | 1 credit | 2S | S. Mendelson | |
| 227-0560-00L | Computer Vision and Artificial Intelligence for Autonomous Cars Up until FS2022 offered as Deep Learning for Autonomous Driving | W | 6 credits | 3V + 2P | C. Sakaridis | |
| 263-5210-00L | Probabilistic Artificial Intelligence | W | 8 credits | 3V + 2U + 2A | A. Krause | |
| 263-5300-00L | Guarantees for Machine Learning Does not take place this semester. | W | 7 credits | 3V + 1U + 2A | F. Yang | |
| 401-3054-14L | Probabilistic Methods in Combinatorics | W | 5 credits | 2V + 1U | B. Sudakov | |
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