Version vom 21. März 2018, 10:51 Uhr

Foundations for Machine Learning

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

Dozent

Yohanes Stefanus

Umfang (SWS)

4/2/0

Module

Leistungskontrolle

Mündliche Prüfung

Information
Literatur

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

The lecture will take place from 11th June till 20th July 2018 in room APB2026 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.

Prerequisites

Probability Theory
Linear Algebra
Algorithm Design & Analysis

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.

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+{{Vorlesung
+|Title=Foundations for Machine Learning
+|Research group=Wissensverarbeitung
+|Lecturers=Yohanes Stefanus;
+|Term=SS
+|Year=2018
+|Module=MCL-AI, MCL-PI, INF-BAS2, INF-VERT2
+|SWSLecture=4
+|SWSExercise=2
+|SWSPractical=0
+|Exam type=mündliche Prüfung
+|Description=<!--
+<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)
+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.
+}}