Introduction to Machine Learning2016HT
No of lectures
Approx 19 lectures, 9 computer labs and 5 seminars.
Anyone with an interest in learning the basics of machine learning.
The course was last given
Students entering the course should have passed at least one course in basic statistics and be familiar with linear statistical models, in particular simple and multiple regression. Also, it is a prerequisite that the students have passed courses in calculus and linear algebra.
Basic concepts in machine learning and data mining. Bayesian and frequentist modelling, model selection. Linear regression and regularization. Linear discriminant analysis and logistic regression. Bagging and boosting. Splines, generalized additive models, trees, and random forests. Kernel smoothers and support vector machines. Mixture models. Gaussian process.
- Pattern recognition and machine learning by C.M. Bishop, ISBN 9780387310732.
- The Elements of Statistical Learning by T. Hastie, R. Tibshirani and J. Friedman, Second Edition, ISBN 9780387848587, pdf available for free.
Oleg Sysoev, Mattias Villani, Isak Hietala.
Written report on individual computer labs.
Page responsible: Director of Graduate Studies
Last updated: 2012-05-03