Foundations for Maschine Learning (SS2018): Unterschied zwischen den Versionen
Romy Thieme (Diskussion | Beiträge) Keine Bearbeitungszusammenfassung |
Romy Thieme (Diskussion | Beiträge) Keine Bearbeitungszusammenfassung |
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===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. | 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. | 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. | 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 | ===Schedule=== | ||
The lecture will take place from 11th June till 20th July 2018 in room APB2026 on the following days: | The lecture will take place from 11th June till 20th July 2018 in room APB2026 on the following days: | ||
Prerequisites | - 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) | |||
===Prerequisites=== | |||
Probability Theory | Probability Theory | ||
Linear Algebra | Linear Algebra | ||
Version vom 9. April 2018, 14:15 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
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)
Prerequisites
Probability Theory Linear Algebra
Algorithm Design & Analysis
