Maschinelles Lernen und Experimentelles Design (SS 2011)

Literatur

Hintergrundlektüre

• (WF) Ian H. Witten, Eibe Frank: Data Mining. Practical Machine Learning Tools and Techniques with Java Implementations, San Diego: Academic Press, 2000.
• (TM) Tom M. Mitchell: Machine Learning, New York: McGraw-Hill, 1997.
• (MS) Christopher D. Manning, Hinrich Schütze: Foundations of Statistical Language Processing

Pro Woche

21. April: Einführung

• WF: 1.1, 1.2, 1.5, 2.1-2.3
• TM: 2

28. April: Decision Tree Learning (1)

• MS: 2.2, 16.1
• WF: 3.1-3.3, 3.7, 4.1, 4.3
• TM: 3

5. Mai: Decision Tree Learning (2)

• WF: 2.4
• Janine Toole (2000): Categorizing Unknown Words: Using Decision Trees to Identify Names and Misspellings. In: Proc. of ANLP 2000, p. 173-179.
  http://www.aclweb.org/anthology-new/A/A00/A00-1024.pdf

12. Mai: Wahrscheinlichkeitstheorie, Naive Bayes

• MS: 2.1
• WF: 4.2

12. Mai: Evaluation (1)

• WF: 5.1-5.5, 5.7

26. Mai: Eavluation (2)

• MS: 5.3

Links

Vorträge halten

• Christopher C. Berndt: How to prepare and make presentations (.hml)
• Tiffani L. Williams: How to give effective scientific presentations (.pdf)
• Jason HarrisonPlanning a Scientific Presentation (.pdf)
• Susan McConellDesigning effective scientific presentations (youtube video)
  Langer, aber sehr guter Vortrag über das Erstellen von PowerPoint etc. Vorträgen