Search result: Course units in Autumn Semester 2020
Electrical Engineering and Information Technology Master  | ||||||
 Master Studies (Programme Regulations 2018) | ||||||
| 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 Advanced core courses bring students to gain in-depth knowledge of the chosen specialization. They are MSc level only. | ||||||
| Number | Title | Type | ECTS | Hours | Lecturers | |
| 227-0423-00L | Neural Network Theory | W | 4 credits | 2V + 1U | H. Bölcskei | |
| 227-0427-00L | Signal Analysis, Models, and Machine Learning Does not take place this semester. This course has been replaced by "Introduction to Estimation and Machine Learning" (autumn semester) and "Advanced Signal Analysis, Modeling, and Machine Learning" (spring semester). | W | 6 credits | 4G | H.‑A. Loeliger | |
| 227-0447-00L | Image Analysis and Computer Vision | W | 6 credits | 3V + 1U | L. Van Gool, E. Konukoglu, 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 | |
| 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 I: From Architectures to VLSI Circuits and FPGAs | W | 6 credits | 5G | F. K. Gürkaynak, L. Benini | |
| 227-0155-00L | Machine Learning on Microcontrollers Registration in this class requires the permission of the instructors. Class size will be limited to 16. Preference is given to students in the MSc EEIT. | W | 6 credits | 3G | M. Magno, L. Benini | |
| 227-0121-00L | Communication Systems | W | 6 credits | 2V + 2U | A. Wittneben | |
| 227-0225-00L | Linear System Theory | W | 6 credits | 5G | M. Colombino | |
| 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 | |
| 227-0445-10L | Mathematical Methods of Signal Processing | W | 6 credits | 4G | H. G. Feichtinger | |
| 227-0477-00L | Acoustics I | W | 6 credits | 4G | K. Heutschi | |
| 263-5210-00L | Probabilistic Artificial Intelligence | W | 8 credits | 3V + 2U + 2A | A. Krause | |
| 401-0647-00L | Introduction to Mathematical Optimization | W | 5 credits | 2V + 1U | D. Adjiashvili | |
| 401-3054-14L | Probabilistic Methods in Combinatorics | W | 6 credits | 2V + 1U | B. Sudakov | |
| 401-3621-00L | Fundamentals of Mathematical Statistics | W | 10 credits | 4V + 1U | S. van de Geer | |
| 401-3901-00L | Mathematical Optimization | W | 11 credits | 4V + 2U | R. Zenklusen | |
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