| Statistical Machine Learning | ||||
| Lectures are Tuesday and Thursday 8:45-10:00 | ||||
Recitations time is Thursday 10:00-11:00(new!)
Teaching Assistant:
| Alex Zimin
| |
| announcements | schedule | references |
| Date | Lecture Topic | Notes | Assignments | ||||||||
| Mar 01 Tue | 1 - A Practical Introduction | sheet: PDF data: wine-train.txt, wine-test.txt | |||||||||
| Mar 03 Thu | 2 - Bayesian Decision Theory, Generative Probabilitistic Models | PDF (new) | |||||||||
| Mar 08 Tue | 3 - Discriminative Probabilistic Models, Maximum Margin Classifiers | PDF (new) | sheet: PDF | ||||||||
| Mar 10 Thu | 4 - Optimization, Kernel Classifiers | PDF (new) | |||||||||
| Mar 15 Tue | 5 - More about kernels and optimization; Model Selection | PDF (new) | |||||||||
| Mar 17 Thu | 6 - Model Selection; Beyond Binary Classification | PDF (new) | sheet: PDF | ||||||||
| Mar 21 -- Apr 01 | spring break | ||||||||||
| Apr 05 Tue | 7 - Learning Theory I | slides: PDF notes: PDF | exercise: PDF data: census.txt | ||||||||
| Apr 07 Thu | 8 - Learning Theory II | slides: PDF notes: PDF | |||||||||
| Apr 12 Tue | 9 - Structured Prediction I | slides: PDF | |||||||||
| Apr 14 Thu | 10 - Structured Prediction II | slides: see PDF for part I | |||||||||
| Apr 19 Tue | 11 - Representation Learning / Deep Learning | slides: PDF
| exercise: sheet 5 (final project)
| Apr 21 Thu
| 12 - Unsupervised Learning
| slides: PDF
|
| until May 01
| final project
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[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] ... and others
| announcements | schedule | references |