FS2016

The course covers the foundations of concurrent systems and multiprocessor synchronization. Students who take this course will learn the basics of multicore programming, the new paradigm of computer science.

See the course home page: http://scg.unibe.ch/teaching/pl

See http://scg.unibe.ch/wiki/softwarecompositionseminar

The main objectives of this course is to introduce the students to the various techniques coming mainly for the statistical domain in the machine learning paradigm. The main objectives of the course are the followings: 1) to be able to use and program in R, the statistical language (used to analyze Big Data); 2) to select, apply and evaluate a learning method using R and to interpret the results; 3) to select the most appropriate method according to the data, and to evaluate the quality of the fit. Practical exercises will complete the theoretical presentation. Description: Introduction to R (statistical software); statistical models and evaluation with R; regression simple and multiple; logistic regression; k-nearest neighbors; linear discriminant analysis (LDA); model evaluation, variable selection and regularization; resampling approaches & evaluation; support vector machines (SVM); boosting; unsupervised approaches (principal component analysis, multidimensional scaling); The final mark is based on both a final written exam and the results of the practical exercises. References: G. Jones, D. Witten, T. Hasti, R. Tibshirani: An Introduction to Statistical Learning. With Applications in R. Springer, 2013. C.M. Bishop: Pattern Recognition & Machine Learning. Springer, 2006. T. Hasti, R. Tibshirani, J. Friedman: The Elements of Statistical Learning. 2nd Ed. Springer, 2009. M.J. Crawley: The R Book. 2nd Ed., Wiley, 2012