Lectures for INFO8004 Advanced Machine Learning, ULiège, Spring 2024.
- Instructors: Pierre Geurts ([email protected]), Gilles Louppe ([email protected]), Louis Wehenkel ([email protected])
- When: Spring 2024, Thursday 9:00 AM
- Classroom: R18 / B28
| Date | Topic |
|---|---|
| February 8 | Course syllabus [slides] Lecture 1: Gaussian and neural processes (Gilles Louppe) [slides] - Paper: "Conditional neural processes", Garnelo et al, 2018 [link] |
| February 15 | Lecture 2: Conformal prediction (Pierre Geurts) [slides] - 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] - Paper 4: "Uncertainty Sets for Image Classifiers using Conformal Prediction", Angelopoulos et al, 2020 [link] |
| February 22 | Lecture 3: Statistical learning theory (Louis Wehenkel) [slides] - Tutorial & discussion: "Statistical Learning Theory - a Hitchhiker's Guide", John Shawe-Taylor and Omar Rivasplata, NeurIPS 2018 [video, slides]. -Notes: "Statistical Learning Theory: A Primer", Louis Wehenkel [notes] |
| February 29 | Lecture 4: Simulation-based inference (Gilles Louppe) [slides] - Paper: "The frontier of simulation-based inference", Cranmer, Brehmer and Louppe, 2020 [link] |
| March 7 | Lecture 5: Explainable machine learning (Pierre Geurts) [slides] - Tutorial: "Explaining Machine Learning Predictions", Lakkaraju, Adebayo, Singh, 2020-2021 [link] - Book: "Interpretable Machine Learning", Christoph Molnar, 2022 [link] |
| March 14 | Lecture 6: Causality (Louis Wehenkel) [slides] - Paper: "Causality. Chapter 1: Introduction to Probabilities, Graphs, and Causal Models", Judea Pearl, 2009 [link] - Paper: "Causal Inference in AI Education: A Primer", Forney and Mueller, 2021 [link] |
| March 21 | Lecture 7: Neural operators (Omer Rochman) - Paper: "Fourier Neural Operator for Parametric Partial Differential Equations", Li et al, 2020 [link, annotated paper)] |
| March 28 | Lecture 8: Latent diffusion models (Gilles Louppe) [slides] - Tutorial: "Latent Diffusion Models: Is the Generative AI Revolution Happening in Latent Space?", Kreis, Gao, and Vahdat [video] |
| April 4 | No class |
| April 11 | Student presentations 1 |
| April 18 | Student presentations 2 |
Deliverable: a 30-minute lecture on the assigned paper and its necessary background. This lecture will be presented to the class on April 4, April 11, or April 18.
This assignment will count for 40% of the final grade.
TBD.
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent