| Title | Time | Room | Instructor |
|---|---|---|---|
| Modern Machine Learning | 10.10.2022 10:15 - 11:30 (Mon) | Alistarh, Dan Cheng, Bingqing Lampert, Christoph Mondelli, Marco Robinson, Matthew | |
| Modern Machine Learning | 12.10.2022 10:15 - 11:30 (Wed) | Alistarh, Dan Cheng, Bingqing Lampert, Christoph Mondelli, Marco Robinson, Matthew | |
| Modern Machine Learning | 12.10.2022 11:45 - 12:45 (Wed) | ||
| Modern Machine Learning | 17.10.2022 10:15 - 11:30 (Mon) | Alistarh, Dan Cheng, Bingqing Lampert, Christoph Mondelli, Marco Robinson, Matthew | |
| Modern Machine Learning | 19.10.2022 10:15 - 11:30 (Wed) | Alistarh, Dan Cheng, Bingqing Lampert, Christoph Mondelli, Marco Robinson, Matthew | |
| Modern Machine Learning | 19.10.2022 11:45 - 12:45 (Wed) | ||
| Modern Machine Learning | 24.10.2022 10:15 - 11:30 (Mon) | Alistarh, Dan Cheng, Bingqing Lampert, Christoph Mondelli, Marco Robinson, Matthew | |
| Modern Machine Learning | 31.10.2022 10:15 - 11:30 (Mon) | Alistarh, Dan Cheng, Bingqing Lampert, Christoph Mondelli, Marco Robinson, Matthew | |
| Modern Machine Learning | 02.11.2022 10:15 - 11:30 (Wed) | Alistarh, Dan Cheng, Bingqing Lampert, Christoph Mondelli, Marco Robinson, Matthew | |
| Modern Machine Learning | 02.11.2022 11:45 - 12:45 (Wed) | ||
| Modern Machine Learning | 07.11.2022 10:15 - 11:30 (Mon) | Alistarh, Dan Cheng, Bingqing Lampert, Christoph Mondelli, Marco Robinson, Matthew | |
| Modern Machine Learning | 09.11.2022 10:15 - 11:30 (Wed) | Alistarh, Dan Cheng, Bingqing Lampert, Christoph Mondelli, Marco Robinson, Matthew | |
| Modern Machine Learning | 09.11.2022 11:45 - 12:45 (Wed) | ||
| Modern Machine Learning | 14.11.2022 10:15 - 11:30 (Mon) | Alistarh, Dan Cheng, Bingqing Lampert, Christoph Mondelli, Marco Robinson, Matthew | |
| Modern Machine Learning | 16.11.2022 10:15 - 11:30 (Wed) | Alistarh, Dan Cheng, Bingqing Lampert, Christoph Mondelli, Marco Robinson, Matthew | |
| Modern Machine Learning | 16.11.2022 11:45 - 12:45 (Wed) | ||
| Modern Machine Learning | 21.11.2022 10:15 - 11:30 (Mon) | Alistarh, Dan Cheng, Bingqing Lampert, Christoph Mondelli, Marco Robinson, Matthew | |
| Modern Machine Learning | 23.11.2022 10:15 - 11:30 (Wed) | Alistarh, Dan Cheng, Bingqing Lampert, Christoph Mondelli, Marco Robinson, Matthew | |
| Modern Machine Learning | 23.11.2022 11:45 - 12:45 (Wed) | ||
| Modern Machine Learning | 28.11.2022 10:15 - 11:30 (Mon) | Alistarh, Dan Cheng, Bingqing Lampert, Christoph Mondelli, Marco Robinson, Matthew | |
| Modern Machine Learning | 30.11.2022 10:15 - 11:30 (Wed) | Alistarh, Dan Cheng, Bingqing Lampert, Christoph Mondelli, Marco Robinson, Matthew | |
| Modern Machine Learning | 30.11.2022 11:45 - 12:45 (Wed) | ||
| Modern Machine Learning | 05.12.2022 10:15 - 11:30 (Mon) | Alistarh, Dan Cheng, Bingqing Lampert, Christoph Mondelli, Marco Robinson, Matthew | |
| Modern Machine Learning | 07.12.2022 10:15 - 11:30 (Wed) | Alistarh, Dan Cheng, Bingqing Lampert, Christoph Mondelli, Marco Robinson, Matthew | |
| Modern Machine Learning | 07.12.2022 11:45 - 12:45 (Wed) | Central Bldg / O1 / Mondi 3 (I01.O1.010) | |
| Modern Machine Learning | 12.12.2022 10:15 - 11:30 (Mon) | Central Bldg / O1 / Mondi 3 (I01.O1.010) | Alistarh, Dan Cheng, Bingqing Lampert, Christoph Mondelli, Marco Robinson, Matthew |
| Modern Machine Learning | 14.12.2022 10:15 - 11:30 (Wed) | Central Bldg / O1 / Mondi 3 (I01.O1.010) | Alistarh, Dan Cheng, Bingqing Lampert, Christoph Mondelli, Marco Robinson, Matthew |
| Modern Machine Learning | 14.12.2022 11:45 - 12:45 (Wed) | ||
| Modern Machine Learning | 09.01.2023 10:15 - 11:30 (Mon) | Central Bldg / O1 / Mondi 3 (I01.O1.010) | Alistarh, Dan Cheng, Bingqing Lampert, Christoph Mondelli, Marco Robinson, Matthew |
| Modern Machine Learning | 11.01.2023 10:15 - 11:30 (Wed) | Central Bldg / O1 / Mondi 3 (I01.O1.010) | Alistarh, Dan Cheng, Bingqing Lampert, Christoph Mondelli, Marco Robinson, Matthew |
| Modern Machine Learning | 11.01.2023 11:45 - 12:45 (Wed) | Central Bldg / O1 / Mondi 3 (I01.O1.010) | |
| Modern Machine Learning | 16.01.2023 10:15 - 11:30 (Mon) | Central Bldg / O1 / Mondi 3 (I01.O1.010) | Alistarh, Dan Cheng, Bingqing Lampert, Christoph Mondelli, Marco Robinson, Matthew |
| Modern Machine Learning | 18.01.2023 10:15 - 11:30 (Wed) | Central Bldg / O1 / Mondi 3 (I01.O1.010) | Alistarh, Dan Cheng, Bingqing Lampert, Christoph Mondelli, Marco Robinson, Matthew |
| Modern Machine Learning | 18.01.2023 11:45 - 12:45 (Wed) | Central Bldg / O1 / Mondi 3 (I01.O1.010) | |
| Modern Machine Learning | 23.01.2023 10:15 - 11:30 (Mon) | Central Bldg / O1 / Mondi 3 (I01.O1.010) | Alistarh, Dan Cheng, Bingqing Lampert, Christoph Mondelli, Marco Robinson, Matthew |
| Modern Machine Learning | 25.01.2023 10:15 - 11:30 (Wed) | Central Bldg / O1 / Mondi 3 (I01.O1.010) | Alistarh, Dan Cheng, Bingqing Lampert, Christoph Mondelli, Marco Robinson, Matthew |
| Modern Machine Learning | 25.01.2023 11:45 - 12:45 (Wed) | Central Bldg / O1 / Mondi 3 (I01.O1.010) | |
| Modern Machine Learning | 30.01.2023 10:15 - 11:30 (Mon) | Central Bldg / O1 / Mondi 3 (I01.O1.010) | Alistarh, Dan Cheng, Bingqing Lampert, Christoph Mondelli, Marco Robinson, Matthew |
| Modern Machine Learning | 01.02.2023 10:15 - 11:30 (Wed) | Central Bldg / O1 / Mondi 3 (I01.O1.010) | Alistarh, Dan Cheng, Bingqing Lampert, Christoph Mondelli, Marco Robinson, Matthew |
| Modern Machine Learning | 01.02.2023 11:45 - 12:45 (Wed) | Central Bldg / O1 / Mondi 3 (I01.O1.010) |
Description:
Introduction to modern machine learning, in particular probabilistic models and deep learning, as well as an overview of its applications at ISTA.
Syllabus:
- Probabilistic Models
- Deep Learning
- Optimization
- Unsupervised Learning
- Applications at ISTA
Capacity:
12/99
Course Code:
C_CS-4000_F22
Course instructor(s):
Bingqing Cheng
Matthew Robinson
Marco Mondelli
Dan Alistarh
Christoph Lampert
Main Contact:
Christoph Lampert
Course type:
Taught course
Course level:
Advanced/specialized
Primary Track:
Computer Science
Secondary Track(s):
Chemistry & Materials
Data Science & Scientific Computing
Course format:
On campus
Duration:
Full semester
ECTS:
6
Semester:
Fall (1&2)
Minimum number of participants:
5
Target audience:
anyone who plans to develop or use Machine Learning / Artificial Intelligence techniques in their PhD
Prerequisites:
Linear Algebra, Calculus, Probability, Scientific Programming in Python
Teaching format:
lectures, homework, potentially project work
Assessment form(s):
participation, regular assignments, exam (might be waived)
Grading scheme:
Numeric grades (1-5)
Course Category:
Credit Course
Academic Year:
AY 2022/23