Lecture "Machine Learning – Supervised Methods"

Time and Location

• Summer Term 2017
• Thursdays 10:00-14:00, room NAFOF 04/493
• First session: 20.04.2017

Summary

The field of machine learning constitutes a modern approach to artificial intelligence. It is situated in between computer science, neuroscience, statistics, and robotics, with applications ranging all over science and engineering, medicine, economics, etc. Machine learning algorithms automate the process of learning, thus allowing prediction and decision making machines to improve with experience.

This lecture will cover a contemporary spectrum of supervised learning methods. All lecture material will be in English.

Prerequisites

The course is designed for Masters students of the Angewandte Informatik program. The lecture Mathematics for Modeling and Data Analysis is recommended as a background. Participants should be familiar with linear algebra and elementary probability theory. For example, the following terms should be well known:

• vector, basis, linear function, linear map, matrix
• norm, inner product, orthogonal
• probability, distribution, density, quantile
• normal distribution, expectation, variance, covariance

Students are expected to solve short programming exercises. Basic programming skills are required, super hero skills are not.

Lecture Videos

The course applies the flipped classroom format. Students work through the relevant lecture material at home. We will use the excellent online course "Learning from Data" as a basis (provided by Caltech under a CC license).

Practical Sessions

The video lecture material is consolidated in a 4 hours/week practical session. Students are expected to go through the relevant lecture material (video lectures) ahead of each session. The lecture material is not be repeated during the presence time, with the exception of the first session, which starts with the first video lecture. Instead, the practical sessions are reserved for practical and mathematical exercises, discussions of the solutions, and general questions regarding the lecture material. Students are expected to bring their laptops to the course.

We will use the following software environment for programming tasks:

• Python 3
• Sci-kit learn

The following links should be useful for setting up the environment and for an introduction to the Python programming language:

• The Anaconda Bundle provides a rather complete Python environment that should contain everything we need within the lecture. Please make sure to use the Python 3 version.
• If you want to learn the Python 3 basics quickly, then take a day and go through this book.
• This NumPy tutorial provides a quick-start into the NumPy package for efficient numerical arrays and scientific computing in Python.

Exam

Written exam of 90 minutes.

• Date: 20.09.2017
• Time: 10:00–11:30
• Room: HNC 20

Retry Exam

Written exam of 90 minutes.

• date: 21.02.2018
• time: 14:00–15:30
• room: HNC 30