Introduction to Machine Learning 2020 | Learning & Adaptive Systems Group

Introduction to Machine Learning

The 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.

News

Due to a policy change of zoom, we had to adjust the passwords for the Q&A sessions. To obtain the password you need to be inside the ETH network and click here, same as before. The same password can be used for the exam preparation tutorials.
EASTER BREAK: There will be no instructor Q&A on April 14&15 and no tutorials on April 15.
Starting now, the instructor Q&A is now recorded as well. The files are password-protected, please use the same credentials as you would use for accessing the 2020 slides (see last point of news).
Due to the complete digitalization of the lecture, we will no longer do the lectures in the usual format. Please watch last year’s videos, which you can access using your normal nethz credentials. During lecture time, we will instead hold zoom meetings, where you can ask questions directly to Andreas Krause. For more details, please refer to your personal email or to piazza.
Due to Corona, there will be no more physical lectures and office hours. Lecture recordings will still be available on the ETH video portal, for questions, please refer to piazza or the tutorial Q&A. Also, there will be no more public viewing of the tutorials, please use your own zoom client.
The video recordings are now available at the ETH Videoportal. Password is the same as for the documents on this website. To obtain the password you need to be inside the ETH network and click here. To establish a VPN connection click 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 on 19. February, 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 out this Python Tutorial.
For the mathematical background check the excellent resource: Mathematics for Machine Learning Online version
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.

FAQ

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. The project is a mandatory part of the examination. Without achieving a passing grade (4), you are not allowed to sit the final examination.
3. Distance examination is allowed but you need to file an official request via study administration. We do not handle these requests.

4. Attendance at Tutorials and Lectures is not mandatory.

Syllabus

Date	Topics	Tutorial	HW	Q&A
Tue 18.2.	Introduction	–	–	–
Wed 19.2.	Regression	Math Recap	HW 1	–
Tue 25.2.	Generalization and Validation	–	–	–
Wed 26.2.	Model Selection	Regression (black), white, supp, code	Sol 1	–
Tue 3.3.	Classification	–	–	–
Wed 4.3.	Feature Selection	Classification	HW 2	–
Tue 10.3.	Kernels I	–	–	–
Wed 11.3.	Kernels II	Kernels, code	Sol 2	–
Tue 17.3	Kernels III	-	-	Q&A
Wed 18.3	Class Imbalance	Kernels 2	HW 3	Q&A
Tue 24.3	Neural Networks	–	–	Q&A
Wed 25.3	Neural Networks II	Imbalance	Sol 3	Q&A, video
Tue 31.3	Neural Networks III	–	–	Q&A, video
Wed 1.4.	Neural Networks IV	Neural Networks	HW 4	Q&A, video
Tue 7.4	Clustering	–	–	no notes, video
Wed 8.4	Dimensionality Reduction	Imputation & CNN	Sol 4	Q&A, video
Tue 14.4	EASTER BREAK
Wed 15.4	EASTER BREAK
Tue 21.4	Dimensionality Reduction II	–	–	no notes, video
Wed 22.4	Probabilistic Modeling	Dimensionality Reduction	HW 5	Q&A, video
Tue 28.4	Logistic Regression	–	–	Q&A, video
Wed 29.4	No lecture	Information Theory	Sol 5	no Q&A
Tue 5.5.	Decision Theory	–	–	Q&A, video
Wed 6.5.	Generative Modeling	Decision Theory	HW 6	no notes, video
Tue 12.5.	Generative Modeling II	–	–	Q&A, video
Wed 13.5.	GMMs	Generative Models, Recap HW 5	Sol 6	no notes, video
Tue 19.5.	GMMs II	–	–	no notes, video
Wed 20.5	GANs	Expectation Maximization, notebooks	HW 7	no notes, video
Tue 26.5.	No class	–	–	no Q&A
Wed 27.5.	No class	Mixture Models	Sol 7	no Q&A
Wed 16.07.	No class	Exam Prep I, slides	–	no Q&A
Wed 22.07.	No class	Exam Prep II, slides	–	no Q&A

Video Lectures

The video recordings of the lectures and tutorials are available at the ETH Videoportal. 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.

Contact

Instructors	Prof. Andreas Krause
Head TA	Philippe Wenk
Assistants	Andisheh Amrollahi, Nemanja Bartolovic, Ilija Bogunovic, Zalán Borsos, Charlotte Bunne, Sebastian Curi, Radek Danecek, Gideon Dresdner, Joanna Ficek, Vincent Fortuin, Carl Johann Simon Gabriel, Shubhangi Gosh, Nezihe Merve Gürel, Matthias Hüser, Jakob Jakob, Mikhail Karasikov, Kjong Lehmann, Julian Mäder, Anastasia Makarova, Mojmír Mutný, Gabriela Malenova, Harun Mustafa, 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, please use the tutorial webinar to ask questions.

Lectures

There will be no more life lectures. Instead, there will be Q&A sessions with Andreas Krause via zoom to discuss the lecture material.

Tutorials

Tutorials will be conducted as a live webinar. For more information, please refer to piazza, your personal email or the lecture slides of the first week. The webinar will take place every Wednesday, starting at 15:15 and lasting for about two hours.

Problem Sets

Homeworks will be distributed electronically and partially graded on the moodle platform . 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, with solutions following one week after or being directly visible after entering your solutions in moodle.

Project

Please find all information about the project here.

Demos

The Demo’s are based on jupyter notebook (with python 3). Please look at this intro for installing and running instructions. We also provide a README File in case you want to install a conda environment where to run the demos. Here are also the requirements.txt for the environment.
Helper files: (Please download them and save inside a ‘utilities’ folder located in the same directory as the demos.). zipped utilities (Updated 18.03.20)
Demos:

Performance Assessment

70% 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 and a simple, non-programmable calculator. No aids are allowed.

Previous Exams:
Exam 2015
Exam 2016
Exam 2017
Exam 2018
Exam 2019
Exam 2020, Sol 20

Text Books

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.