How can we build systems that perform well in uncertain environments and unforeseen situations? How can we develop systems that exhibit “intelligent” behavior, without prescribing explicit rules? How can we build systems that learn from experience in order to improve their performance? We will study core modeling techniques and algorithms from statistics, optimization, planning, and control and study applications in areas such as sensor networks, robotics, and the Internet. The course is designed for upper-level undergraduate and graduate students. VVZ information is available here.
- [Nov 18, 2022] In the Q&A sessions after Projects 2, 3, and 4, we will give a brief overview of the project and a sketch of a possible solution. These sessions will happen on Nov 21, Nov 28, and Dec 19, respectively.
- [Oct 02, 2022] Change of lecture hall for tutorial: From now on, the tutorials on Thursdays 16:00 – 18:00 will take place in HG F7 instead of CHN C14.
Access to lecture materials
The lecture materials and the Q&A zoom meetings 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.
The lecture script is available here
. The script is new and, thus, may contain typos and small mistakes. If you find mistakes or have tips on how to improve the script, we encourage you to send an email to firstname.lastname@example.org
. We aim to continually improve the script based on your feedback. Since the script is updated whenever we notice and fix a mistake, we encourage you to re-download the script from time to time. Errors noticed since the beginning of the semester are listed in the errata at the end of the pdf.
Lectures will be held on Friday, 10:00 to 12:00 and 13:00 to 14:00, in ETA F5 with a simultaneous video screening in ETF E1. A link to a live-stream will also be made available and allow following the lectures from home on your personal device. If you follow the live-stream, you will be able to ask questions via the course channel on the EduApp
. The lecture will be recorded and the recording will be made available after the lecture here
. The first lecture will take place on Friday, 23rd September 2022, and the last lecture will take place on Friday, 23rd December 2022. It is not mandatory to attend the lectures. Links below will become active when the resource becomes available.
Tutorials will be held on Thursday, 16:00 to 18:00 in HG F7. The tutorials are not live-streamed but recorded
and also available afterwards here
The first tutorial will take place on Thursday, 29th September 2022, and the last tutorial will take place on Thursday, 22nd December 2022. It is not mandatory to attend the tutorials. The links below will become active when the resource becomes available.
Q&A sessions (virtual office hours) will be held on Monday, 17:00 to 18:00 virtually on Zoom. Like the lectures and tutorials, the Q&As start 15 min past the full hour.
To join the Zoom call, you have to be logged into your ETH zoom account (i.e., <nethz-login>@ethz.ch and your email password). The session will be recorded and the recording will be made available afterwards.
The Q&A sessions are an informal opportunity to ask questions about the course.
You will be able to ask questions via the native Zoom chat or by speaking out.
In the Q&A sessions after the deadlines of Projects 2, 3, and 4, we will give a brief overview of the project and a sketch of a possible solution for students that are interested.
We will also be there to answer any questions related to the project. These three project Q&A’s will be hosted on Nov 21, Nov 28, and Dec 19, respectively.
The first Q&A session will take place on Monday, 26th September 2022, and the last Q&A session will take place on Monday, 19th December 2022. It is not mandatory to attend the Q&A sessions.
|| Prof. Andreas Krause
|| Jonas Rothfuss
|| Jialin Li, Zhiyuan Hu, Streli Paul, Arad Mohammadi, Ya-Ping Hsieh, Pragniya Alatur, Viacheslav Borovitskiy, Hugo Yeche, Marco Maninetti, Zijun Hui, Hongruyu Chen, Jonas Hübotter, Huang Daoji, Gizem Yüce, Aayush Grover, Matteo Turchetta, Lenart Treven, Charlotte Bunne, Parnian Kassraie, Lars Lorch, Bhavya Sukhija, Vukasin Bozic, Yarden As, Qi Ma, Scott Sussex, Vignesh Ram Somnath
|| Please use Moodle for any questions regarding the course or ask your question in the lectures, tutorials or Q&A sessions. 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
We do not maintain a mailing list, but instead kindly request you to use Moodle
to ask questions with regard to the course. Please ask your questions in the Moodle forum whose topic best fits your question. If you need to contact the Head TA directly, please send an email to firstname.lastname@example.org
instead of contacting me at my personal email address. I will not respond to requests sent to my personal email address. Please think twice before you send an email though and make sure you read all information here carefully. Based on previous experience, we received a lot of questions or requests that are resolvable with the information provided here.
The course includes a total of five projects. Projects are code assignments that require solving machine learning problems with methods taught in the course. For each project, you are allowed to work in a group of one to three students. It is your own responsibility to form a group and you can find teammates in the lectures or on Moodle. The first project (Task 0) will be ungraded; its purpose is to help you familiarize yourself with the code submission workflow. The remaining projects are graded and are accounted for in determining your final grade for the course. More information including a tentative schedule is available in the project information sheet
and on the project server
. Both are accessible from within the ETH network or via VPN.
We will publish a total of six (optional) homework assignments during the lecture series. The homework assignments will be published on this website and some questions from the homework assignment will additionally be made available as a Moodle quiz
. These assignments are intended for you to apply and reinforce the material presented in the lecture and to get accustomed to the Moodle platform. You are encouraged, but not required to do the homework. Doing the homework assignments is not mandatory. Your performance in the homework will have no influence on your final grade. Homework assignments are expected to be published bi-weekly, with solutions following one week after or being directly visible in Moodle. The exact day and time a homework is being published may deviate slightly over the course of the semester.
Demos will be shown during the lecture and are made available to you here
. They are hosted in a GitLab repository to which you need to be given access. Everyone who enrolled (on mystudies) to the course before Wednesday, 28th September 2022, will be automatically granted access to this Gitlab repository by Friday, 30th September 2022. If you enrolled at a later date, please individually request access by sending an email to email@example.com
, only after 30.09.2022. Use the subject line “Access Request: PAI 2022 Demos” and include your nethz in this email. The demos are Jupyter Notebooks.
The exam will last for 120 minutes and might be computer-based (Moodle) or paper-based with multiple choice questions. The language of examination is English.
In order to pass this course and be allowed to sit the session examination, you need to pass the projects. This is, you need to achieve a project grade of 4 or higher. If you don’t pass the projects, you are required to de-register from the exam and will otherwise be treated as a no-show. The final grade is computed as a weighted average of the session exam grade (70%) and the project grade (30%). There are no special arrangements for PhD students who are taking this course. In order to obtain a “Testat”, you need a passing grade for the course. This is you need to pass the projects as described above, take the exam and achieve an overall passing grade (4 or higher) for the course. If you passed the projects last year, you still need to do the projects again this year. The project grade cannot be carried over from the previous year.
- S. Russell, P. Norvig. Artificial Intelligence: A Modern Approach (4th edition).
- C. E. Rasmussen, C. K. I. Williams Gaussian Processes for Machine Learning.
- Christopher M. Bishop. Pattern Recognition and Machine Learning. [optional]
- Richard S. Sutton and Andrew G. Barto. Reinforcement Learning: An Introduction.