1.     Introduction

How did AlphaGo beat Lee Sedol? How does Tesla work? How does iPhone's facial unlocking work? Why can ChatGPT understand and reply? How does the traffic in megacity run smoothly? In this Advanced Seminar, we will together take the voyage throughout machine learning and artificial intelligence: from linear regression to high-dimensional prediction, from decision tree to reinforcement learning, from topic model to natural language process, from statistics to neural networks. Gradually, you find the answers to those questions and learn how their technologies have been applied in different domains, such as smart energy, intelligent transport, natural language recognition, decision intelligence.

2.     Schedule

-       Time: Every alternative Tuesday, 2:15PM-5:45PM, from 15 Oct 2025 to 21 Jan 2026.

-       Location: B.0.23, Seminarraum (1910.EG.023B)

-       Final Report Submission: 23:59 PM, Wed, 11 Feb 2026

3.     Course Evaluation

-       Four / Five in-Class Presentation (40%) [Group Work]: In this course you will be working as a group. In each seminar, each group will present one paper (no more than 25min): your two to three team members are encouraged to take turns. A great resource about giving a scientific talk from Microsoft scientist: https://youtu.be/sT_-owjKIbA. I highly recommend you watch this great talk. And to fully get yourself prepared, you are recommended to finish the paper reading and prepare the slides before the class. During the reading, Confluence is the recommended platform for you to take notes.

-       Contribution to Discussion (10%) [Group Work]: After the presentation of one group, another group will ask the questions and discuss the following but not limited to topics (around 10min): (1) contributions of the paper with respect to the existing literature, (2) limitations of the paper, (3) potential improvements, (4) how this paper’s method could be applied to your research problem, etc.

-       One Written Review Essay (50%) [Individual Work]: I encourage you to pick a topic which is covered by our seminars and search one paper by yourself from the following high quality publication venues: including but not limited to: AAAI, IJCAI, ICML, NeurIPS, ECMLPKDD, KDD, ICDE, AAMAS, CVPR, IEEC, ECCV, CVPR, and so on. After reading the paper, you need to write a 6-page long review report (single-line spacing, 11pt, excl. cover page, references and appendices don’t count), including but not limited to the following contents: (1) Introduction [20%] of the paper’s research problem [4%], challenge [4%], existing methods [4%], this paper’s innovation [4%] with respect to the state-of-the-art methods, and the paper’s main results [4%]; (2) a summary of the methodology [10%] the paper proposed, remember to focus on the intuition, instead of the math; (3) a detailed review about the paper’s strength [5%] and the minor or major issues [5%] in this paper, imaging you are the reviewer of the conference and you decide to give the paper “major revision” decision, what kind of review comments are you going to write; (4) The most importantly, how will you improve [10%] the paper speaking of methodological point of view.

-       Bonus (extra 10%) [Individual Work]: If the paper you choose has an open-source data and code, and you are interested in reproducing the paper’s result, I highly recommend you add one-page long implementation report by you and your takeaway / insights from the implementation.

Lecture Biomolecular NMR-Spectroscopy (2 SWS) CH3184a

Chemie (M.Sc.): Schwerpunkt "Biolog. Chemie" (Wahlpflicht, inkl. Praktikum!), alle HF/NF (Wahlveranstaltung);
Biochemie (M.Sc.): chem. NF
4 ECTS (only lecture) / 5 ECTS (incl. Practical)

Lecturer: Michael Sattler

Time:   Friday 10:15 h - 12:00 h; starting Fri Oct 21, 2022

Location: Lecture room CH 22209. The lecture will be given live. Handouts and further information will be provided in Moodle.

Prüfung: Modulprüfung (oral exam after lecture or after practica, resp.)

Module: for the full module (5 ECTS) participation in the Practical Biomol. NMR (CH3184b, summer term) is also required!

Content:

Basic experiments and applications of biomolecular NMR-spectroscopy

• Structure and conformation of proteins and nucleic acids
  
• Basic NMR experiments and information provided
  
• Structure determination and NMR of biological macromolecules (proteins, RNA)
  
• Ligand binding, drug discovery
  
• Studying conformational dynamics by NMR
• Current topics (dynamics, IDPs, large proteins, drug discovery, in cell NMR, PREs)