Probabilistic Artificial Intelligence (2020)

Probabilistic Artificial Intelligence (Fall ’20)

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.
  • Dates for the projects are updated. You may find them on the Project information handout.
  • Zoom tutorials are recorded. The videos are password protected; to obtain the tutorials password you need to be inside the ETH network and click here.
  • The link to Zoom classroom for tutorial sessions is updated.
  • The first lecture starts on 18.09, and the exercise sessions begin on 24.09.
  • During the lecture, for questions from the remote audience, we’ll use the ETH EduApp. There is a course channel for PAI 2020, where you can post questions (also anonymous if preferred), and the TA present in class will moderate the incoming questions.
  • The tutorial will be once a week and online only. As no physical office hours are allowed, one extra hour after the tutorials will be added for these purposes.
  • The lectures will mostly be given in a lecture hall with limited attendance (at most 50% of lecture hall capacity). It will be possible to join remotely via zoom with acccess to slides, whiteboard, and speaker camera. Students can interact, e.g. ask questions, physically as well as digitally. The lectures will be recorded via zoom’s recording functionality.
  • 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.
  • The lecture video recordings will available at ETH Videoportal.

Date Topic References Tutorial Homework Solution
Sep 18 Introduction & Probability AI A Modern Approach: Ch. 1 & Ch. 13.1-5 Mathematics for ML: Ch. 6 & 8 & 9 - Hw1
Sep 25 Bayesian Linear Regression AI A Modern Approach: Ch. 14.1 & 14.4 Gaussian Processes for ML: Ch. 2 to 2.1.1 Probability recap & Gaussians [Recording] Hw2

Instructor Prof. Andreas Krause
Head TA Anastasia Makarova
Assistants Andisheh Amrollahi, Ilija Bogunovic, Zalán Borsos, Charlotte Bunne, Sebastian Curi, Gideon Dresdner, Vincent Fortuin, Carl Johann Simon Gabriel, Matthias Hüser, Mojmír Mutný, Mohammad Reza Karimi, Max Paulus , Jonas Rothfuss, Stefan Stark, Olga Mineeva, Hugo Yeche, Amir Joudaki, Luka Rimanic, Laura Manduchi, Zhao Zhikuan, Immer Alexander
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 tutorials to ask questions.
Friday 10-12 ETA F 5 Live Stream
Friday 13-14 ETA F 5 Live Stream
To make sure we avoid crowding, we rotate attendance through student cohorts grouped according to their last names’ initial letter. You can participate physically according to the following scheme:
  • Initials A-G: Weeks 1, 4, 7, etc.
  • Initials H-P: Weeks 2, 5, 8, etc.
  • Initials Q-Z: Weeks 3, 6, 9, etc.
  • Tutorials
    Thursday 16-18 Online Zoom
    When entering the webinar, please use your nethz email address (i.e. [name] or [name]

    The exam is 120 minutes long. It might take place at a computer. The language of examination is English. As written aids, you can bring one A4 sheet of paper (you can write on both sides), either handwritten or 11 point minimum font size. Please bring your Legi (ID card) for the exam. Please do not use cellphones / tablets in the exam. Simple non-programmable calculators are allowed in the exam. Ph. D. Students can only get credits by passing the exam. You can find previous exams here: [2018], [2017], [2016], [2015], [2014], [2013], [2012].
    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 Piazza. Assignments will require handing in the solution code as well as a short report. In particular, there will be 5 code assignments. The first project is ungraded and will allow students to become familiar with our code submission workflow. The remaining projects are graded (pass/fail) and mandatory for passing the PAI course. Out of the 4 code projects, we construct the overall grade as follows: project grade = 6 – number of failed projects. For passing the course and being allowed to write the exam, students are required to pass at least 2 out of the 4 assignments. Overall, the projects grade counts 30% towards the final grade in the course. The code projects will be released throughout the semester. You can find the tentative project schedule and further details in the project information sheet [pdf]. The projects can be accessed and submitted on our project sever You will need to be in the ETH network or use the VPN to access the server.
    There will be a different homework fortnightly. These will not be corrected and do not count for the final grade. However, we recommend to do them as they will help preparing for the final exam and understanding the concepts.
    More updates are coming soon.
    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. More updates are coming soon.
    Text Books
    • S. Russell, P. Norvig. Artificial Intelligence: A Modern Approach (3rd edition).
    • Christopher M. Bishop. Pattern Recognition and Machine Learning. [optional]
    • Richard S. Sutton and Andrew G. Barto. Reinforcement Learning: An Introduction.