Machine Learning Seminar

Advisors Michel Baes, Patrick Cheridito, Florian Rossmannek and Philipp Zimmermann

Main Reference
- S. Shalev-Shwartz and S. Ben-David (2014)
  Understanding Machine Learning: From Theory to Algorithms (UML)
Additional Reading
- R. Vershynin (2019) High-Dimensional Probability (HDP)
Presentation Tips

Meetings
- Sept 18 Organization of the seminar
- Sept 25 What is learning? ERM, PAC, agnostic PAC, uniform convergence
  (UML 2–4; HDP 2.6; advisor: Florian)
- Oct 2 Examples of hypothesis classes, halfspaces, regression, no-free-lunch theorem
  (UML 5, 9; advisor: Florian)
- Oct 9 A combinatorial approach to learnability, VC-dimension, fundamental theorem of statistical learning (UML 6; HDP 8.3.1–8.3.3; advisor: Michel)
- Oct 16 Hypothesis dependent sample sizes, non-uniform learnability, PAC-Bayes (UML 7, 31; advisor: Michel)
- Oct 23 Convex learning problems, RLM, stability (UML 12–13; advisor: Philipp)
- Oct 30 Stochastic gradient descent (UML 14; advisor: Philipp)
- Nov 6 Kernel methods, multiclass, ranking, complex prediction problems (UML 16–17; advisor: Michel)
- Nov 13 Multiclass learnability, Natarajan dimension, fundamental theorem for multiclass problems (UML 29; advisor: Michel)
- Nov 20 Feedforward neural networks (UML 20; advisor: Florian)
- Nov 27 Generative models, MLE, EM, Bayesian reasoning (UML 24; advisor: Florian)
- Dec 4 Dimensionality reduction, PCA, sparse recovery, compressed sensing
  (UML 23; HDP 10.1–10.3; advisor: Philipp)
- Dec 11 Online learning, Littlestone dimension, online gradient descent (UML 21; advisor: Philipp)
- Dec 18 More precise sample size estimates, Rademacher complexities, covering numbers, Dudley's inequality (UML 26–27; HDP 8.1–8.4; advisor: Florian)

Seminar on the Theory and Applications of Machine Learning