/dats0001-foundations-of-data-science

Materials for DATS0001 Foundations of Data Science, ULiège

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DATS0001 Foundations of Data Science

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

Agenda

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: Data
nb02: 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 criticism
nb10: 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 study
nb12: 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

Homeworks