The course is the second part of a two semester course on Statistical Learning. The first part (WS 2005/2006) concentrated on chapters 1-10 of the book The Elements of Statistical Learning, Springer 2001, this follow up course consists of two parts. The first part will continue with chapters 11-14 of the book. The second part will deal with methods of Statistical Learning applied to problems in Bioinformatics. There will be two hours of lecture per week and one hour of tutorial (V2/Ü1).
This course covers a subject that is relevant for computer scientists in general as well as for other scientists involved in data analysis and modeling. It is not limited to the field of computational biology.
Lecturer: Jörg Rahnenführer
Tutor: Laura Tolosi
Course language: English
|Course:||Weekly, Wednesdays 11.15-13.00, Building 46, Room 533.|
|Tutorial:||Biweekly, Fridays 11-13, Building 46, Room 023.|
|Office hours:||On appointment.|
The lecture is targeted to advanced students in math, computer science and science students with
Prerequisites: Vordiplom in Mathematics or Computer Science or equivalent. Students should know linear algebra and have basic knowledge in statistics.
Hastie, Tibshirani, Friedman: The Elements of Statistical Learning, Springer 2001. Readers of the course are encouraged to acquire this book.
|Lecture 1||Fri April 21||Repetition - Overview - Outlook|
|Lecture 2||Fri April 28||Neural Networks (HTF chapter 11)|
|Lecture 3||Wed May 3||Support Vector Machines (HTF chapter 12)|
|Lecture 4||Wed May 10||Prototype Methods and Nearest-Neighbors (HTF chapter 13)|
|Lecture 5||Wed May 17||Unsupervised Learning I (HTF chapter 14)|
|Lecture 6||Wed May 24||Unsupervised Learning II (HTF chapter 14)|
|Lecture 7||Wed May 31||Kernel Methods (HTF chapter 6)|
|Lecture 8||Wed June 14||Normalization of Gene Expression Data|
|Lecture 9||Wed June 21||Classification of Gene Expression Data|
|Lecture 10||Wed June 28||Statistical Analysis with the Gene Ontology|
|Lecture 11||Wed July 5||Classification of Protein Structures|
|Lecture 12||Wed July 12||Learning with Mixtures of Trees|
|Lecture 13||Wed July 19||Analysis of ArrayCGH Data|
|Tutorial||Date||Topic||HW Assigned||HW Due|
|Tutorial 0||Fri April 21||Repetition (Lecture 1)||HW 1|
|Tutorial 1||Fri May 5||Model Assessment + Boosting||HW 2||HW 1|
|Tutorial 2||Fri May 19||Neural Networks + Support Vector Machines||HW 3||HW 2|
|Tutorial 3||Fri June 2||Nearest-Neighbors + Unsupervised Learning||HW 4||HW 3|
|Tutorial 4||Fri June 16||Kernel Methods||HW 5||HW 4|
|Tutorial 5||Fri June 30||Analysis of Expression Data||HW 6||HW 5|
|Tutorial 6||Fri July 14||Analysis with the Gene Ontology||HW 6|
Both parts of this course fulfil the requirements for the curricula of computer science and bioinformatics as optional course with 6 resp. 4 credit points (Spezialvorlesung, 6 bzw. 4 Leistungspunkte).
50% of the homework points and final exam (most likely oral).