Materials for DATS0001 Foundations of Data Science, ULiège, Fall 2023.
- Instructor: Gilles Louppe
- When: Monday 1:30 PM
- Classroom: B28/1.21
- Discord: https://discord.gg/qpvh4ueEFQ
| Date | Topic |
|---|---|
| September 18 | No class |
| September 25 | Course syllabus Lecture 1: Introduction nb01: Build, compute, critique, repeat [notebook]Reading: Blei, Build, Compute, Critique, Repeat, 2014 [Section 1] Reading: Box, Science and Statistics, 1976 |
| October 2 | Lecture 2: Datanb02a: Tables [notebook]nb02b: JAX [notebook]nb02c: Data wrangling [notebook]Reading: Harris et al, Array programming with NumPy, 2020 |
| October 9 | Lecture 3: Visualization nb03a: Plots [notebook]nb03b: Data visualization principles [notebook]nb03c: High-dimensional data [notebook]Reading: Rougier et al, Ten Simple Rules for Better Figures, 2014 Reading: Rougier, Scientific Visualization: Python+Matplotlib, 2022 |
| October 16 | No class |
| October 23 | Lecture 4: Bayesian modeling nb04: Latent variable models [notebook, sidenotes (LVMs), sidenotes (Probabilistic PCA)]Reading: Gelman et al, Bayesian workflow, 2020 [Sections 1 and 2] Reading: Blei, Build, Compute, Critique, Repeat, 2014 [Sections 2 and 3] |
| October 30 | No class |
| November 6 | Lecture 5: Markov Chain Monte Carlonb05: Markov Chain Monte Carlo [notebook] [sidenotes]Reading: Gelman et al, Bayesian Data Analysis, 3rd, 2021 [Chapter 11] |
| November 13 | Lecture 6: Expectation-Minimization nb06: Expectation-Maximization [notebook] [sidenotes]Reading: Dempster et al, Maximum Likelihood from Incomplete Data via EM, 1977 |
| November 20 | Lecture 7: Variational inference nb07: ADVI [notebook] [sidenotes]Reading: Kucukelbir et al, Automatic Differentiation Variational Inference, 2016 |
| November 27 | Lecture 8: Model criticismnb08a: Model checking [notebook]nb08b: Model comparison [notebook]Reading: Gelman et al, Bayesian Data Analysis, 3rd, 2021 [Chapters 6 and 7] |
| December 4 | Lecture 9: Wrap-up case studynb09: Space Shuttle Challenger disaster [notebook]Reading: Cam Davidson-Pilon, Bayesian Methods for Hackers, 2015 [Chapter 2] |
- Homework 1: Exploration of solar power data and weather data (due by November 6)
- Homework 2: Modeling photovoltaic power production (due by December 1)
- Homework 3: Improving and comparing forecasting models (due by December 15)
- Exam-at-home: TBD
Homeworks must be submitted on Github classroom. Follow the links sent by email to register to each homework.