Statistical Machine Learning
Lectures are Monday and Wednesday 10:15-11:30 online via BigBlueButton (URL has been sent out, or ask me via email).
Recitations are Wednesdays, 11:45-12:30 online via Zoom (meeting details have been sent out, or ask TAs via email).
Instructors: Christoph Lampert
Teaching Assistant: Alex Peste,   Bernd Prach

announcements schedule references


Schedule (tentative, all dates are estimates)

Date   Lecture Topic Slides Assignments
Oct 05 Mon 1 A Hands-On Introduction PDF (v1.1, 05/10/20) handout: exercise sheet 1 : PDF
data: wine-train.txt, wine-test.txt
Oct 07 Wed 2 Bayesian Decision Theory,
Generative Probabilitistic Models
Oct 12 Mon 3 Discriminative Probabilistic Models PDF handout: exercise sheet 2 PDF  
Oct 14 Wed 4 Maximum Margin Classifiers PDF
Oct 19 Mon 5 Model Evaluation; Regularization; Model Selection PDF handout: exercise sheet 3 PDF  
data: XtrainIMG.txt, Ytrain.txt
Oct 21 Wed 6 Bias/Fairness; Transfer Learning PDF
Oct 26 Mon - no lecture (national holiday) handout: exercise sheet 4 PDF
data: XtestIMG.txt, Ytest.txt XtrainIMG2.txt, Ytrain2.txt
Oct 28 Wed 7 Learning Theory I PDF  
Nov 2 Mon 8 Learning Theory II
Nov 4 Wed 9 Deep Learning I  
Nov 9 Mon 10 Deep Learning II
Nov 11 Wed 11 Unsupervised Learning  
Nov 16 Mon 12 Project Presentations  
Nov 18 Wed 13 Buffer  


[1] Christopher Bishop: Pattern Recognition and Machine Learning, Springer, 2007.
[2] Mohri, Rostamizadeh, Talwalkar: Foundations of Machine Learning, MIT Press, 2012.
[3] Shalev-Shwartz, Ben-David: Understanding Machine Learning: From Theory to Algorithms, Cambridge University Press, 2014.
[4] Hal Daume III: A Course in Machine Learning, online.
[5] Marc Peter Deisenroth, A. Aldo Faisal, Cheng Soon Ong: Mathematics for Machine Learning, online.
[6] Solon Barocas, Moritz Hardt, Arvind Narayanan: Fairness and Machine Learning, online
... and many others

announcements schedule references