Foundations of Deep Learning
| Course type: | Lecture + Exercise |
| Time: | Lecture: Tuesday, 10:15 - 11:45; Optional exercises: Friday, 10:15 - 11:45 |
| Location: | The course will be in-person. - Weekly flipped classroom sessions will be held on Tuesday in HS 00 006 (G.-Köhler-Allee 082) - Optional exercise sessions will take place on Friday in HS 00 006 (G.-Köhler-Allee 082) |
| Organizers: | Steven Adriaensen , Abhinav Valada , Mahmoud Safari , Rhea Sukthanker , Johannes Hog |
| Web page: | ILIAS - available starting 8am, 15.10.24 (please make sure to also register for all elements of this course module in HISinOne) |
Foundations of Deep Learning
Deep learning is one of the fastest growing and most exciting fields. This course will provide you with a clear understanding of the fundamentals of deep learning including the foundations to neural network architectures and learning techniques, and everything in between.
Course Overview
The course will be taught in English and will follow a flipped classroom approach.
Every week there will be:
- a video lecture
- an exercise sheet
- a flipped classroom session (Tuesdays, 10:15 - 11:45)
- an attendance optional exercise session (Fridays, 10:15 - 11:45)
At the end, there will be a written exam (likely an ILIAS test).
Exercises must be completed in groups and must be submitted 2 weeks (+ 1 day) after their release.
Your submissions will be graded and you will receive weekly feedback.
Your final grade will be solely based on a written examination, however, a passing grade for the exercises is a prerequisite for passing the course.
Course Material: All material will be made available in ILIAS and course participation will not require in-person presence. That being said, we offer ample opportunity for direct interaction with the professors during live Q & A sessions and with our tutors during weekly attendance optional in-class exercise sessions.
Exam: The exam will likely be a test you complete on ILIAS. In-person presence will be required .
Course Schedule
The following are the dates for the flipped classroom sessions (tentative, subject to change):
15.10.24- Kickoff: Info Course Organisation
22.10.24 - Week 1: Intro to Deep Learning
29.10.24 - Week 2: From Logistic Regression to MLPs
5.11.24 - Week 3: Backpropagation
12.11.24 - Week 4: Optimization
19.11.24 - Week 5: Regularization
26.11.24 - Week 6: Convolutional Neural Networks (CNNs)
03.12.24 - Week 7: Recurrent Neural Networks (RNNs)
10.12.24 - Week 8: Attention & Transformers
17.12.24 - Week 9: Practical Methodology
07.01.25 - Week 10: Auto - Encoders, Variational Auto - Encoders, GANs
14.01.25 - Week 11: Uncertainty in Deep Learning
21.01.25 - Week 12: AutoML for DL
28.01.25 - Round - up / Exam Q & A
In the first session (on 15.10.24) you will get additional information about the course and get the opportunity to ask general questions. While there is no need to prepare for this first session, we encourage you to already think about forming teams.
The last flipped classroom session will be held on 28.01.25.
Questions?
If you have a question, please post it in the ILIAS forum (so everyone can benefit from the answer).
Seminar: Automated Reinforcement Learning
| Course type: | Block Seminar |
| Time: | Kickoff Session: 17.10.24 14:00 - 16:00 Presentation Sessions: TBD (likely first week of February) |
| Location: | Kickoff Session: SR 02-016/18 (G.-Köhler-Allee 101) Presentation Sessions: TBD |
| Organizers: | André Biedenkapp , Noor Awad , Raghu Rajan , M Asif Hasan , Baohe Zhang |
| Web page: | HISinOne , Local Page |
Seminar: Pruning and Efficiency in LLMs
Large language models (LLMs) exhibit remarkable reasoning abilities, allowing them to generalize across a wide range of downstream tasks, such as commonsense reasoning or instruction following. However, as LLMs scale, inference costs become increasingly prohibitive, accumulating significantly over their life cycle. In this seminar we will dive into methods like quantization, pruning and knowledge distillation to optimize LLM inference. Please fill this interest form to participate in the seminar.
| Course type: | Seminar |
| Time | Five slots, to be determined with all participants. Kick-off is likely on the 24th of October from 2-3pm |
| Location | in-person; SR 04-007, building 106 |
| Organizers | Rhea Sukthanker , Arbër Zela , Mahmoud Safari |
| Registration | Via HISinOne (maximum nine students, registration opens 14th of October) |
| Language | English |
more
Seminar: Large Language Models, Deep Learning, and Foundation Models for Tabular Data
The field of tabular das has recently been exploding with advances through large language models (LLMs), deep learning algorithms, and foundation models. In this seminar, we want to dive deep into these very recent advances to understand them.
| Course type: | Seminar |
| Time | Five slots, to be determined with all participants. Kick-off is likely on the 23rd of October at 10 to 11 am. |
| Location | in-person; Meeting Room in our ML Lab |
| Organizers | Lennart Purucker |
| Registration | Via HISinOne (maximal six students, registration opens 14th of October) |
| Language | English |
more