Selected Topic: Machine Learning
Course code: AT70.9022
This course is elective
Intelligent systems, such as speech recognition systems, document classification systems, and character recognition systems, are concerned with the transformation of input data (e.g. speech, documents, or bitmaps) into desired output data (words, document classes, or characters, respectively). To obtain an efficient system, this transformation function must be carefully constructed and its parameters must be properly adjusted. Machine learning is concerned with the automatic learning of these parameters from training examples. It draws heavily on computer science, algorithms and data structures, probability, statistics, and optimization. This course covers fundamental concepts as well as state of the art algorithms in machine learning. The grading system relies on homework, student presentations of research from the primary academic literature, and a project.
Introduction. Probability. Linear classifiers. Support vector machines. Clustering and mixture models. Subspace projection. Sequential data. Graphical models. Student presentations of primary research papers.
Prerequisite(s)Programming Experience, Mathematical Sophistication.
- Christopher Bishop. Pattern Recognition and Machine Learning. Springer. 2006.
- Richard O. Duda, Peter E. Hart, and David G. Stork. Pattern Classification. 2nd Edition. Wiley-Interscience. 2000.