Learning and Intelligent Systems 2017

Learning and Intelligent Systems

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
  • First class on 28.2. First tutorial/recitation on 27.2.
  • The video recordings of the last year’s lectures are available at 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.

 
Syllabus
Date Topics Tutorial HW Solutions
Introduction
Introduction - - -
Tue 28.2. Perceptron - - -

Video Lectures

Contact
Instructors Prof. Gunnar Rätsch ,  Prof. Thomas Hofmann
Head TA Kfir Levy
Assistants Olivier BachemFelix BerkenkampJosip DjolongaNatalie DavidsonEsfandiar Mohammadi ,Alkis GotovosStephanie HylandJohannes Kirschner ,Aytunç ŞahinMatteo Turchetta
Mailing List If you have any questions please send them to lis2017@lists.inf.ethz.ch from your ethz.ch address.
Lectures
Tue 13-15 ML D 28
Wed 13-15 ML D 28
Tutorials
Mon 15-17 HG D 5.2
Tue 10-12 ML E 12
Tue 15-17 HG D 5.2
Fri 13-15 HG D 5.2

Project
Part of the coursework will be a project, carried out in groups of up to 3 students. The goal of this project is to get hands-on experience in machine learning tasks. The project grade will constitute 30% of the total grade. More details on the project will be given in the tutorials.
Exam
The mode of examination is written, 120 minutes length. The language of examination is English. As written aids, you can bring two A4 pages (i.e. one A4 sheet of paper), either handwritten or 11 point minimum font size. The written exam will constitute 70% of the total grade.
Text Books
  • 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.

Matlab
  • The official Matlab documentation is available online at the Mathworks website.
  • If you have trouble accessing Matlab’s built-in help function, you can use the online function reference on that page or use the command-line version (type help at the prompt).
  • There are several primers and tutorials on the web, a later edition of this one became the book Matlab Primer by T. Davis and K. Sigmon, CRC Press, 2005.