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
  • HW3 is published
  • HW2 is published
  • HW1 is published
  • Project info appears here, ProjectInfo.
  • First class on 28.2. First tutorial/recitation on 27.2.
  • This year’s video recordings are available at ETH Videoportal
  • Last year’s video recordings  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.

Lecture Notes & Overview Slides
Here we publish the blackboard shots of the first two lectures: Blackboard Shots
In this link you will find the LIS 2017 overview slides: LIS Overview
In this link you will find this year's lecture notes: Lecture notes

Syllabus
Date Topics Tutorial HW Solutions
Introduction
Introduction (last year's) - - -
Tue 28.2. Perceptron (last year's) - [HW1] [HW1-sol]
Tue 14.3.
Linear regression, gradient descent (last year's) - - -
Wed 22.3 Kernels (this year's) - - -
Wed 29.3
Kernels continued, multi-class ,Lecture notes (this year's) - [HW2] [HW2-sol]
Tue 11.4
Sparsity & Feature selection [ipynb] , [Example] [HW3] -
Wed 12.4
Clustering - - -
Tue 25.4
Dimension Reduction,Lecture notes - - -
Tue 25.4
Nonlinear Dimension Reduction - - -

Video Lectures

Lectures
Tue 13-15 ML D 28 ML E 12 (via video)
Wed 13-15 ML D 28 ML E 12  (via video)
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. Given that your exam grade is greater than 3.5, then the project grade will constitute 30% (bonus) of the total grade, i.e.,
  • if exam_grade>=3.5,    final_grade = max(exam_grade, 0.7*exam_grade + 0.3*project_grade)
  • else,   final_grade = exam_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.