Materials for DATS0001 Foundations of Data Science, ULiège, Fall 2022.
- Instructor: Gilles Louppe
- When: Fall 2022, Monday 1:30 PM
- Classroom: 1.21 / B28
- Discord server
Date | Topic |
---|---|
September 26 | Course syllabus Lecture 1: Introduction nb01 : Build, compute, critique, repeat [notebook]Reading: Blei, Build, Compute, Critique, Repeat, 2014 [Section 1] |
October 3 | Lecture 2: Datanb02 : Tables [notebook]nb03 : Data wrangling [notebook]Reading: Harris et al, Array programming with NumPy, 2020 |
October 10 | Lecture 3: Visualization [sidenotes]nb04 : Plots [notebook]nb05 : Visualizing high-dimensional data [notebook]Reading: Rougier et al, Ten Simple Rules for Better Figures, 2014 |
October 17 | Lecture 4: Bayesian probabilistic modeling [sidenotes]nb06 : Latent variable models [notebook]Reading: Gelman et al, Bayesian workflow, 2020 [Sections 1 and 2] Reading: Blei, Build, Compute, Critique, Repeat, 2014 [Sections 2 and 3] |
October 24 | Lecture 5: Expectation-Minimization [sidenotes]nb07 : Expectation-Maximization [notebook]Reading: Dempster et al, Maximum Likelihood from Incomplete Data via EM, 1977 |
October 31 | Homework 1: Explore the Argo data |
November 7 | Lecture 6: Variational inference [sidenotes]nb08 : ADVI [notebook]Reading: Kucukelbir et al, Automatic Differentiation Variational Inference, 2016 |
November 14 | Lecture 7: Markov Chain Monte Carlo [sidenotes]nb09 : Markov Chain Monte Carlo [notebook]Reading: Gelman et al, Bayesian Data Analysis, 3rd, 2021 [Chapter 11] |
November 21 | Lecture 8: Model criticismnb10 : Model checking [notebook]nb11 : Model comparison [notebook]Reading: Gelman et al, Bayesian Data Analysis, 3rd, 2021 [Chapters 6 and 7] |
November 28 | No class |
November 28 | Homework 2: Random walks of Argo floats |
December 5 | No class |
December 12 | Lecture 9: Wrap-up case studynb12 : Space Shuttle Challenger disaster [notebook]Reading: Cam Davidson-Pilon, Bayesian Methods for Hackers, 2015 [Chapter 2] |
Decembber 12 | Homework 3: Posterior inference over random walks |
Decembber 17-21 | Exam-at-home |
- Homework 1: Explore the Argo data (due by October 31)
- Homework 2: Random walks of Argo floats (due by November 28)
- Homework 3: Posterior inference over random walks (due by December 12)
- Exam-at-home: TBD (sent on December 17, due by December 21)