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.
- The exam will take place on a computer. Please inform us about any special request regarding a disability.
- In the first week’s tutorial sessions , 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 or Python Tutorial II
- For the mathematical background check the excellent resource: Mathematics for Machine Learning Online version
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- 1. If this course is compulsory for your study program (Kernfach), you are able to register irrespective of the waiting list. Please allow some time for the transfer from the waiting list.
- 2. PhD students are required to obtain a passing grade in the course (4.0 or higher based on project and exam as detailed in the course catalog) to gain credit points.
- 3. If you have a significant clash with other courses, and you cannot attend the assigned tutorial slot, you can attend other session, but first give seating to other students.
- 4. The project is a mandatory part of the examination. Without achieving a passing grade (4), you are not allowed to sit the final examination.
- 5. Distance examination is allowed but you need to file an official request via study administration. We do not handle these requests.
- 6. Attendance at Tutorials and Lectures is not mandatory.
Date Topics Tutorial HW Solutions Tue 18.2. – – – – Wed 19.2. – – – – Tue 25.2. – – – –
Video LecturesThe video recordings of the lectures and tutorials will be available soon.
|Instructors||Prof. Andreas Krause|
|Head TA||Philippe Wenk|
|Assistants||Andisheh Amrollahi, Nemanja Bartolovic, Ilija Bogunovic, Zalán Borsos, Charlotte Bunne, Sebastian Curi, Radek Danecek, Giden Dresdner, Joanna Ficek, Vincent Fortuin, Carl Johann Simon Gabriel, Shubhangi Gosh, Nezihe Merve Gürel, Matthias Hüser, Jakob Jakob, Mikhail Karasikov, Kjong Lehmann, Julian Mäder, Mojmír Mutný, Anastasia Makarova, Gabriela Malenova, Mohammad Reza Karimi, Max Paulus , Laurie Prelot, Jonas Rothfuss, Stefan Stark, Jingwei Tang, Xianyao Zhang,|
|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, we have office hours with very limited capacity, each Friday, 13:00-15:00, at ML D 28.|
|Tue 13-15||ETA F 5||ETF E 1 (via video)|
|Wed 13-15||ETA F 5||ETF E 1 (via video)|
|Tutorials will be online. More information to follow.|
Problem SetsHomeworks will be distributed electronically and partially graded on the moodle platform (more information to follow). They are intended for you to practice concepts and your performance in the homeworks will in no way affect your final grade. They are published bi-weekly (solutions follow one week after).
ProjectMore information available soon.
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.