Lectures for INFO8004 - Advanced Machine Learning, ULiège, Spring 2021.
- Instructors: Pierre Geurts ([email protected]), Gilles Louppe ([email protected]), Louis Wehenkel ([email protected])
- When: Spring 2021, Thursday 9:00 AM
- Classroom: Lifesize
| Date | Topic |
|---|---|
| February 4 | Course syllabus [slides] Lecture 1: Gaussian and neural processes (Gilles Louppe) - Paper: "Conditional neural processes", Garnelo et al, 2018 [link] - Talk [slides] |
| February 11 | Lecture 2: Statistical learning theory (Louis Wehenkel) - Tutorial & discussion: "Statistical Learning Theory - a Hitchhiker's Guide", John Shawe-Taylor and Omar Rivasplata, NeurIPS 2018 [video, slides]. - Additional resources: "An introduction to Statistical Learning Theory", Louis Wehenkel [slides]; "Statistical Learning Theory: A Primer", Louis Wehenkel [notes] |
| February 18 | Lecture 3: Conformal prediction (Pierre Geurts) - Paper 1: "A tutorial on conformal prediction" , Shafer and Vovk, 2008 [link] - Paper 2: "Inductive conformal prediction: theory and application to neural networks", Papadopoulos, 2008 [link] - Paper 3: "Conformalized quantile regression", Romano et al, 2019 [link] - Talk [slides] |
| February 25 | Lecture 4: Geometric Deep Learning - Tutorial: "Geometric Deep Learning", Michael Bronstein, MLSS 2020 [video] [slides] - Paper: "Geometric deep learning: going beyond Euclidean data", Bronstein et al, 2016 [link] |
| March 4 | Lecture 5: Simulation-based inference (Gilles Louppe) - Paper: "The Frontier of Simulation-based Inference", Cranmer, Brehmer and Louppe, 2019 [link] - Talk [slides] |
| March 11 | Lecture 6: Causality (Louis Wehenkel) - Paper: "Causality. Chapter 1: Introduction to Probabilities, Graphs, and Causal Models", Judea Pearl, 2009 [link] - Talk [slides] |
| March 18 | Lecture 7: Upside-Down Reinforcement Learning (Matthia Sabatelli) - Paper 1: "Reinforcement Learning Upside Down", Juergen Schmidhuber, 2019 [link] - Paper 2: "Training Agents using Upside-Down Reinforcement Learning", Rupesh Kumar Srivastava et al, 2019 [link] - Talk [slides] |
| March 25 | Lecture 8: Normalizing Flows (Antoine Wehenkel) - Paper: TBD - Talk [slides] |
| April 1 | Lecture 9: Responsible AI (Adeline Decuyper, Accenture) |
| April 15 | Student presentations 1 |
| April 22 | Student presentations 2 |
| April 29 | Student presentations 3 |
| May 6 | Student presentations 4 |
Deliverables:
- a 30-minute lecture on the paper and its necessary background. This lecture will be presented to the class on April 15, April 22, April 29 or May 6.
- a summary report (4-6 pages) summarizing the problem, the contribution of the paper and a critical discussion. Use the
template-report.textemplate (although feel free to adjust if necessary).
Slides and reports should be submitted on the Montefiore submission platform. This assignment will count for 40% of the final grade.
The paper-group assignments are available here. If your name does not appear in the list, please contact us immediately, as otherwise you will be marked as Absent for the assignment.
TBD.
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent