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.
- Due to COVID-19, there will be no physical lectures and office hours. If the epidemiological situation allows, we might return to the classical lecture format with physical attendance.
- 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 24. 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.
- 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.
- We have set up forum on Piazza. Please use this for questions regarding the course material, projects and organization.
- 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.
- The project is a mandatory part of the examination. Without achieving a passing grade (4), you are not allowed to sit the final examination.
- Distance examination is allowed but you need to file an official request via study administration. We do not handle these requests.
- Attendance at Tutorials and Lectures is not mandatory.
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.
||Prof. Andreas Krause and Prof. Fanny Yang
||Alex Tifrea, Andisheh Amrollahi, Andrii Zadaianchuk, Carl-Johann Simon-Gabriel, Chris Wendler, Cristina Pinneri, David Lindner, Gideon Dresdner, Hugo Yeche, Ilnura Usmanova, Joanna Ficek, Jonas Gehring, Jonas Rothfuss, Kjong Lehmann, Laurie Prelot, Lenart Treven, Max Paulus, Mohammad Reza Karimi, Mojmír Mutný, Nicolo Ruggeri, Niels Gleinig, Olga Mineeva, Pier Giuseppe Sessa, Scott Sussex, Sebastian Curi, Seyedmorteza Sadat, Shubhangi Gosh, Stefan Stark, Vignesh Ram Somnath, Ya-Ping Hsieh
||Please use the Piazza forum for questions regrading course material, organization and projects. In order to send private quesitions to instructors use the private thread function of Piazza. If this does not work for your request, please use the tutorial webinar to ask questions.
|Questions & Answers
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.
Please find all information about the project here
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.
(Please download the zipped helper files (updated 18.03.20)
them and save inside a ‘utilities’ folder located in the same directory as the demos.)
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 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.
, Sol 20