- Hastie, Tibshirani, Friedman: Elements of Statistical Learning [ESLI] (download)
- Duda, Hart, Stork: Pattern Classification (library)
: English (lectures), English or German (exams, exercises)Lectures
: Mo, 08:15 - 09:45 48-462 Exercises:
Wed, 16:00 - 17:00 48-211 (attend even if there is no homework)Start
: 26.10.2009 Written Examination:
March 1 2010, 10:00 - 11:30, 11-262, (second date: July 28, 2010, nobody participated on that day). Next exam November 8, 2010 Please register with "Prüfungsamt"All the Sage notebooks are now accessible as PDF files from this site.For preparing for the exam, you may also find the lecture notes from previous years useful.
- nearest neighbor classification and nearest neighbor algorithms
- feature extraction and common feature types
- neural networks, gradient descent
- RBF networks and interpolation
- perceptrons and support vector machines
- k-means clustering, Gaussian mixtures, and semi-supervised learning
- VQ, principal components analysis and compression
- hierarchical clustering, dimensionality reduction
- decision trees
- pattern recognition with graphs
- generative data models and model-based classification
- Bayesian decision theory
- ML and Bayesian parameter estimation
These algorithms are widely used in many areas of computer science and will be illustrated on a variety of problems, including text and data mining, network security, image classification, OCR, and handwriting recognition.
The emphasis of the course is on a working understanding of algorithms and their properties, but also covers some important theoretical results.
The course is accompanied by practical exercises in Numerical Python.