Introduction to Machine LearningThe course will introduce the foundations of learning and making predictions from data. We will study basic concepts such as trading goodness of fit and model complexity. We will discuss important machine learning algorithms used in practice, and provide hands-on experience in a course project. VVZ Information is available here.
- In the first week’s tutorial sessions (Mon, Tue, Wed, Fri), we will offer a review session of required background material for the course. This will include a short recap of linear algebra, multivariate analysis and probability theory.
- For programming background, we recommend knowing Python. For those without experience in it, check one of the excellent tutorials Python Tutorial I , Python Tutorial II , Python Tutorial III
- For the mathematical background check the excellent resource: Mathematics for Machine Learning Online version
- Please attend the tutorials according to last name: A-C: Mon 15-17,HG D 1.2 D-H: Tue 15-17,HG D 1.2 I-M: Wed 15-17,CAB G 11 N-Z: Fri 13-15, ML D 28
- The files are password protected. To obtain the password you need to be inside the ETH network and click here. To establish a VPN connection click here.
Video LecturesThe video recordings of the lectures are available at the ETH Videoportal.
|Instructors||Prof. Andreas Krause|
|Head TA||Mojmir Mutny|
|Assistants||Andisheh Amrollahi, Mohammad Karimi, Prashanth Chandran, Joanna Ficek, Vincent Fortuin,Gürel Nezihe Merve, Harun Mustafa, Jingwei Tang, Kjong Lehmann, Natalie Davidson, Olga Mineeva, Laurie Prelot, Stefan Stark, Johannes Kirschner, Matteo Turchetta, Sebastian Curi, Max Paulus, Phillipe Wenk, Ilja Bogunovic, Aytunc Sahin, Kfir Levy, Anastasiia Makarova|
|Piazza||If you have any questions, please use the Piazza Course Forum.|
|Mailing List||Please use the Piazza Forum for questions regrading course material, organisation and projects. If this does not work for your request, you can send an email to TBA from your ethz.ch address (respond time 5 days).|
|Tue 13-15||HG E 7||HG E 3 and 5 (via video)|
|Wed 13-15||HG E 7||HG E 3 and 5 (via video)|
|A-C: Mon 15-17||HG D 1.2|
|D-H: Tue 15-17||HG D 1.2|
|I-M: Wed 15-17||CAB G 11|
|N-Z: Fri 13-15||ML D 28|
ProjectPlease find all information about the project here.
DemosThe Demo’s are based on jupyter notebook (with python 3). Please look at this intro for installing and running instructions. Helper files: (Please download them and save them on same directory as the demos). zipped helper files (Updated 22.3.2018) Demos:
- Linear Regression (updated 06.04.2018)
- Classification (updated 06.04.2018)
- Kernelized Classification/k-NN (updated 06.04.2018)
- Kernelized Regression (updated 06.04.2018)
- Neural Networks (updated 18.05.2018)
- Unsupervised Learning (updated 18.05.2018)
- Bias, Variance, and Noise tradeoff (updated 18.05.2018)
- Probabilistic Modelling (updated 18.05.2018)
- Semi-supervised Learning (updated 18.05.2018)
Performance Assessment70% session examination, 30% project; the final grade will be calculated as weighted average of both these elements. As a compulsory continuous performance assessment task, the project must be passed on its own. The exam might take place at a computer. The practical projects are an integral part (60 hours of work, 2 credits) of the course. Participation is mandatory. Failing the project results in a failing grade for the overall examination of Introduction to Machine Learning (252-0220-00L). Students who do not pass the project are required to de-register from the exam and will otherwise be treated as a no show.
For the final exam, you can bring two A4-pages (i.e. one A4-sheet of paper), either handwritten or 11 point minimum font size. No calculators or other aids are allowed.
- Marc Peter Deisenroth, A Aldo Faisal, and Cheng Soon Ong Mathematics for Machine Learning Online version
- K. Murphy. Machine Learning: a Probabilistic Perspective. MIT Press 2012
- C. Bishop. Pattern Recognition and Machine Learning. Springer, 2007 (optional)
- T. Hastie, R. Tibshirani, and J. Friedman. The Elements of Statistical Learning: Data Mining, Inference and Prediction. Springer, 2001. Available online
- L. Wasserman. All of Statistics: A Concise Course in Statistical Inference. Springer, 2004.