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 series of course projects.
VVZ Information is available here.
- 23.02.2023: The corrected sheet of the Winter exam is now available: IML Exam Winter 2023.
- 22.08.2022: The corrected sheet of the summer exam is now available: IML Exam Summer 2022.
- 10.08.2022: Due to requests, the review session slides are now available.
- 05.08.2022: The exam takes place in HIL (In Hönggerberg; map) and Halle 622 (in Oerlikon; map). You should already have received an email about the organization of the exam and your designated room. Please read that email carefully. Note that the exam starts at 14:00 and ends at 16:00 (with additional 10 minutes for filling out the answer sheet with a black pen). Specifically, please bring a pencil, eraser and a black pen.
- 02.08.2022: The recording of the review session is now available. See the news item below.
- 01.08.2022: The recording and the notes of the 8th tutorial (discussing the solution of homework 3) is now available in the Tutorials section.
- 28.07.2022: The review session will be on Friday 29 July, from 9-12. It will be in-person (HG F 1) and live-streamed (Live-stream link). The recording will be available in a few days, and the link will come here: Recording
- 29.06.2022: The lecture notes are updated.
- 16.05.2022: On Wednesday 18.05. the Q&A session will be about the project tasks 2 and 3. If you have specific problems or questions about your code/approach, the best place to ask is on Moodle. There, you can include your code, so that the project team, as well as other students, can help you more efficiently. We have already made two posts, describing a possible solution for task 2 (here) and task 3 (here) on Moodle. You can follow-up further questions there as well.
- 13.04.2022: There will be no classes in the Easter break (from Friday 15.04. till Monday 25.04., both inclusive). The tutorial on 15.04. will not take place. Instead, we upload a recorded session, reviewing homework 3. Moreover, on Tuesday 26.04. there will be a PyTorch tutorial at the time of the lecture (in the same course hall).
- 30.03.2022: The project information has been updated (CET has changed to CEST). Moreover, on 30.03.2022, the Q&A session will be about the Task 1 of the projects.
- 08.03.2022: All students are added to the demos Gitlab repository. See the “Demos” section for more information.
- 02.03.2022: On the week of 7.3.22, instead of the lectures, there will be a probability theory review on Tuesday and an in-class Q&A on Wednesday. The place is the same as the lectures. On the Friday of that week, there will be a Python Tutorial, and there will be no Zoom Q&A.
- 01.03.2022: Homework 1 is available. You can download the PDF below (in Tutorials section). There is an accompanying Moodle quiz, where you can test your solutions and get immediate feedback. The quizzes and the homework are not graded.
- 23.02.2022: Project information is updated.
- 21.02.2022: Project information is updated.
- 17.02.2022: As per new ETH masterplan, wearing a mask is obligatory if you attend the lectures in-person.
Due to the large number of registrations for this course, the in-person attendance (in the main course hall) is limited. Students with an odd Legi number can attend the odd weeks, and students with even Legi numbers can attend the even weeks, both for the lectures (main hall) and tutorials. Attendance to the extra room (ETF E1) is possible for those with odd Legi on even weeks and vice versa (only for lectures, there is no extra room for tutorials). The priority is with those who are already enrolled, and Students in the waiting list are kindly requested to follow the lectures online.
- 10.02.2022: In the first week’s tutorial session (25 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.
Due to ETH Covid-19 regulations, only students with a valid Covid certificate (COVID Certificate app, valid ETH CovMass test, or foreign vaccination certificates accepted by ETH) and ETH student card are allowed to enter the classroom, both for lectures and tutorials. Please bring them to class.
- 10.02.2022: The first lecture is on 22 February and the first tutorial is on 25 February.
- 10.02.2022: Welcome to the course Introduction to Machine Learning!
- 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 do the final examination.
- PhD students attending the class and willing to get a grade, should do the projects and the exam. We do not provide a special course this semester.
- Distance examination is allowed but you need to file an official request via study administration. We do not handle these requests.
- Attendance at tutorials, Q&As, and lectures is not mandatory.
- For programming background, we recommend knowing Python. For those without experience in it, check out this Python tutorial.
- Lecture slides, exercises, lecture notes, Q&A Zoom calls and the recordings of the Q&A are password protected. To obtain the password you need to be inside the ETH network or use the ETH VPN and click here. Check here to learn how to establish a VPN connection.
Lectures will be on Tuesdays and Wednesdays, 2 pm to 4 pm, in ETA F5
with a simultaneous video screening in ETF E1. Link to the live stream
is also available, allowing following the lectures from home.
If you follow the live stream, you can ask
questions via the course channel on EduApp
All lectures will be recorded, and the recording will be made available within a day after the lecture in the ETH Video Portal
The first lecture will take place on Tuesday, 22 February 2022, and the
last one will be on Wednesday, 1 June 2022. It is not
mandatory to attend the lectures. However, if you want to attend the lectures
you are required to provide a Covid-19 certificate per ETH policy. In addition
to providing a certificate,
ETH requires you to wear a mask during the
New in this semester, we provide a detailed manuscript that contains the most important mathematical background needed for understanding the course. These notes will also serve as a reference for the lectures and set up the notation and needed theorems, definitions, and concepts. The manuscript is not final and will be updated and expanded with notes for some of the lectures as the course progresses, so please check back regularly.
Tutorials will be held on Fridays, 2 pm to 4 pm in ML D28. Live streaming is also available and you can follow the tutorials from home. Similar to the lectures, you will be able to ask questions via the course channel on the EduApp. The tutorial will be recorded and the recording will be made available after the tutorial on ETH Video Portal. The first tutorial will take place on Friday, 25 February 2022, and the last tutorial will take place on Friday, 3 June 2022. It is not mandatory to attend the tutorials. Similar Covid-related regulations apply to the tutorials.
* This is a re-iteration of the tutorial, recorded at home. Due to technical problems, the first tutorial was not properly recorded.
|| Math Recap
|| Recording* [Part 1, Part 2]
|| Homework 1
|| Regression and Optimization
|| Notes, Code
|| Data Analysis with Python
|| Review of Homework 1
|| Classification and Model Selection
|| Review of Homework 2
|| Neural Networks
|| No Class
|| ConvNets and Clustering
|| Solution 4
|| ConvNets and Clustering
|| Generative Models
|| Generative Models 2
|| Generative Models 3
Q&A sessions (virtual office hours) will be held on Wednesdays, 5 pm to 6 pm virtually on Zoom. The Q&A sessions are an informal opportunity to ask questions about the course. We may use some Q&A sessions for giving more information about the projects. The Q&A session will be recorded and the recording will be made available after the Q&A session. You will be able to ask questions via the native Zoom chat or by speaking out, if requested to do so. The first Q&A session will take place on Wednesday, 23 February, and the last Q&A session will take place on Wednesday, 1 June. It is not mandatory to attend the Q&A sessions. To obtain the password for joining Q&A see FAQ point 7.
|| Prof. Andreas Krause and Prof. Fanny Yang
|| Mohammad Reza KarimiAssistants
|| Alexander Stutz, Alexandru Tifrea, Anastasia Makarova, Andisheh Amrollahi, Anver Khusainov, Arjun Bhardwaj, Chris Wendler, David Lindner, Eugene Bykovetc, Gizem Yüce, Hugo Yèche, Jacob Clarysse, Jonas Rothfuss, João de Borges Sá Carvalho, Kai Lion, Lars Lorch, Lenart Treven, Luca Corinzia, Matteo Turchetta, Mojmir Mutný, Olga Mineeva, Oliver Holl, Panagiotis Misiakos, Parnian Kassraie, Riccardo Zuliani, Robin Geyer, Scott Sussex, Sebastian Curi, Seyedmorteza Sadat, Shyam Sundhar Ramesh, Stefan Stark, Vignesh Ram Somnath, Xia Li, Xinyu Sun, Ya-Ping Hsieh
|| Please use Moodle for questions regarding course material, organization and projects. If you need to contact the Head TA or the lecturer directly, please send an email to email@example.com. Please think twice before you send an email though and make sure you read all information here carefully.
Questions & Answers
The code projects will require solving machine learning problems with methods taught within the course. You are allowed to work in groups of 1 – 3 students, but it is your responsibility to find a group. You can search for teammates by posting on Moodle. Assignments will require handing in the solution code as well as a short report. In particular, there will be 6 code assignments. The first assignment is ungraded and will allow you to become familiar with our code submission workflow. The remaining projects are graded (pass/fail) and mandatory for passing the Introduction to Machine Learning course. Details regarding the projects can be found here.
Demos are based on jupyter notebook (with Python 3). Please look at this
intro for installing and running instructions. We recommend that you create a conda environment
to maintain the code base. The demos are hosted at GitLab
. You should have been automatically added to the repository as a Reporter. This gives you the opportunity to clone the project on your machine and run the demos. If you sill cannot access the repository, please send an email to firstname.lastname@example.org with the subject “[IML 2022] Gitlab access” and include your nethz in the email.
70% session examination, 30% code 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 coding projects are an integral part (60 hours of work, 2 credits) of the course. Participation is mandatory. To be eligible for the examination of Introduction to Machine Learning (252-0220-00L), you need to pass the code projects, i.e., attain an overall project grade of 4 or higher.
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 (that is, one A4-sheet of paper), either handwritten or 11 point minimum font size. A simple non-programmable calculator is allowed during the exam.
The exam will be multiple choice. Here
you can find an example of the question types as well as how to fill out the answer sheet to guarantee successful automatic grading.
, Sol 2020
, Sol 2021
- Marc Peter Deisenroth, A Aldo Faisal, and Cheng Soon Ong Mathematics for Machine Learning. Cambridge University Press, 2020.
- 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.
- L. Wasserman. All of Statistics: A Concise Course in Statistical Inference. Springer, 2004.
- G. James., D. Witten and et al. An Introduction to Statistical Learning. Springer, 2021.