Foundations for Maschine Learning (SS2018): Unterschied zwischen den Versionen

Version vom 9. April 2018, 14:09 Uhr

{{Vorlesung |Title=Academic Skills in Computer Science |Research group=Wissensverarbeitung |Lecturers=Steffen Hölldobler; |Tutors=Johannes Fichte (starting from 01.05.) |Term=SS |Year=2018 |Module=INF-04-FG-SWT, INF-AQUA, MCL-CS |SWSLecture=2 |SWSExercise=2 |SWSPractical=0 |Exam type=mündliche Prüfung |Description====Description===

This lecture is aiming at providing basic skills in reading, understanding, and evaluating scientific articles, and in presenting scientific results in presentations and publications. It includes topics like citations and bibliographic data in scientific publications, selection of topics, examples, and results for an oral presentation, preparation of slides and other materials for oral presentations as well as selecting, structuring, and writing scientific articles. The lecture will include practical training in reading and reviewing scientific articles, in preparing and giving presentations, and in setting up and writing short scientific articles. The topics will be selected from Computational Logic. Hence, some familiarity with propositional and first-order logic is assumed.

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 {{Vorlesung
-|Title=Foundations for Machine Learning
+|Title=Academic Skills in Computer Science
 |Research group=Wissensverarbeitung
-|Lecturers=Yohanes Stefanus;
+|Lecturers=Steffen Hölldobler;
+|Tutors=Johannes Fichte (starting from 01.05.)
 |Term=SS
 |Year=2018
-|Module=MCL-AI, MCL-PI, INF-BAS2, INF-VERT2
+|Module=INF-04-FG-SWT, INF-AQUA, MCL-CS
-|SWSLecture=4
+|SWSLecture=2
 |SWSExercise=2
 |SWSPractical=0
 |Exam type=mündliche Prüfung
-|Description=<!--
+|Description====Description===
-<center><code>  <span style="color:#FF0000">  </span> </code></center>
--->
+This lecture is aiming at providing basic skills in reading, understanding, and evaluating scientific articles, and in presenting scientific results in presentations and publications. It includes topics like citations and bibliographic data in scientific publications,  selection of topics, examples, and results for an oral presentation, preparation of slides and other materials for oral presentations as well as selecting, structuring, and writing scientific articles. The lecture will include practical training in reading and reviewing scientific articles, in preparing and giving presentations, and in setting up and writing short scientific articles. The topics will be selected from Computational Logic. Hence, some familiarity with propositional and first-order logic is assumed.
-===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)
-===Prerequisites===
-* Probability Theory
-* Linear Algebra
-* Algorithm Design & Analysis