README.md

DATS0001 Foundations of Data Science

Materials for DATS0001 Foundations of Data Science, ULiège, Fall 2025.

• Instructor: Gilles Louppe
• Teaching assistant: Matthias Pirlet
• When: Monday 1:30 PM
• Classroom: B28/1.21
• Discord: https://discord.gg/qpvh4ueEFQ

Agenda

Date	Topic
September 15	Course syllabus [PDF] Lecture 1: Build, compute, critique, repeat [PDF] nb01: Build, compute, critique, repeat [notebook] Reading: Blei, Build, Compute, Critique, Repeat, 2014 [Section 1] Reading: Box, Science and Statistics, 1976
September 22	Lecture 2: Data and exploratory analysis [PDF] nb02a: Arrays with NumPy nb02b: Arrays with JAX nb03b: Data wrangling with Pandas nb02d: Exploratory data analysis
September 29	Lecture 3: Visualization [PDF] nb03: Plots with Matplotlib Reading: Rougier et al, Ten Simple Rules for Better Figures, 2014
October 6	Lecture 4: Latent variable models [PDF] nb04b: Probabilistic PCA Reading: Blei, Build, Compute, Critique, Repeat, 2014 [Sections 1-3]
October 13	Lecture 5: State-space models [PDF] nb05: State-space models
October 20	Lecture 6: Markov chain Monte Carlo [PDF] nb06a: Markov chains nb06b: MCMC Reading: Gelman et al, Bayesian Data Analysis, 3rd, 2021 [Chapter 11]
November 3	Lecture 7: Expectation-maximization [PDF] nb07: Expectation-maximization Reading: Dempster et al, Maximum Likelihood from Incomplete Data via EM, 1977
November 10	Lecture 8: Variational inference [PDF] nb08a: Coordinate ascent variational inference nb08b: Automatic differentiation variational inference Reading: Kucukelbir et al, Automatic differentiation variational inference, 2016
November 17	Lecture 9: Model criticism and validation [PDF] nb09a: Model checking nb09b: Model comparison nb09c: Bayesian Occam's razor
November 24	No lecture (general strike)
December 1	Lecture 10: Wrap-up case study [PDF] nb10: Estimating air pollution from satellite data
December 19	Homework deadline

Homework

See instructions in the homework notebook. Deadline: December 19, 2025.