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{{Vorlesung
{{Vorlesung
|Title=Foundations for Machine Learning
|Title=Academic Skills in Computer Science
|Research group=Wissensverarbeitung
|Research group=Wissensverarbeitung
|Lecturers=Yohanes Stefanus;
|Lecturers=Steffen Hölldobler;  
|Term=SS
|Term=SS
|Year=2018
|Year=2018
|Module=MCL-AI, MCL-PI, INF-BAS2, INF-VERT2
|Module=INF-04-FG-SWT, INF-AQUA, MCL-CS
|SWSLecture=4
|SWSLecture=4
|SWSExercise=2
|SWSExercise=2
|SWSPractical=0
|SWSPractical=0
|Exam type=mündliche Prüfung
|Exam type=mündliche Prüfung
|Description=<!--
|Description=Content will be added later
<center><code>  <span style="color:#FF0000">  </span> </code></center>
-->
 
 
===Content===
 
The topic of this course is mathematical foundations
for Machine Learning. We define the term "machine learning" to mean
the automated detection of meaningful patterns in data.
 
Nowadays machine learning based technologies are ubiquitous:
digital economic systems, web search engines, anti-spam software,
credit/insurance fraud detection software, accident prevention systems,
bioinformatics, etc.
 
This course provides a theoretical account of the fundamental ideas
underlying machine learning and the mathematical derivations that transform
these principles into practical algorithms, such as algorithms appropriate
for big data learning. We will start with Valiant's PAC (Probably
Approximately Correct) learning model, the ERM (Empirical Risk
Minimization) learning rule, the No-Free-Lunch Theorem, and
the VC (Vapnik-Chervonenkis) dimension. The course will end with deep learning.
 
 
===Schedule===
 
<b>The lecture will take place from 11th June till 20th July 2018 in room <span style="color:#FF0000">  APB2026
</span>
on the following days:
 
*  Mondays 4. DS (1pm - 2:30pm); starting on 11th June 2018
* Tuesdays 2. DS (9:20am - 10:50am)
* Thursdays 2. DS (9:20am - 10:50am)
 
 
2 Credit Points can be achieved.</b>
 
 
===Prerequisites===
 
* Probability Theory
* Linear Algebra
* Algorithm Design & Analysis
|Literature=* Shai Shalev-Shwartz and Shai Ben-David. Understanding Machine Learning: From Theory to Algorithms. Cambridge University Press, 2014.
 
* Ian Goodfellow and Yoshua Bengio and Aaron Courville. Deep Learning. MIT Press, 2016.
}}
}}

Version vom 21. März 2018, 10:53 Uhr

Academic Skills in Computer Science

Lehrveranstaltung mit SWS 4/2/0 (Vorlesung/Übung/Praktikum) in SS 2018

Dozent

  • Steffen Hölldobler

Umfang (SWS)

  • 4/2/0

Module

Leistungskontrolle

  • Mündliche Prüfung


Content will be added later




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