Description: Machine learning is concerned with the development of computer programs that allow computer (or machine) to learn from examples or experiences. Machine learning is of interdisciplinary nature, with roots in computer science, statistics and pattern recognition. In the past decade, this field has witnessed rapid theoretical advances and growing real world applications. Successful applications include machine perception (speech recognition, computer vision), control (robotics), data mining, web search and text classification, time-series prediction, system modelling, bioinformatics, data compression, and many more.

This course will give a comprehensive introduction to machine learning both by presenting technologies proven valuable and by addressing specific problems such as pattern recognition and data mining. This course covers both theory and practices for machine learning, but with an emphasis on the practical side namely how to effectively apply machine learning to a variety of problems. Topics will include

• Supervised learning (of classification and regression functions)

K-nearest neighbors, decision trees, naïve Bayes, support vector machines, logistic regression, evolutionary algorithms, Bayesian Networks, hidden Markov model, neural networks, boosting

• Unsupervised learning and clustering

K-means, hierarchical clustering (agglomerative and divisive), principal component analysis, Expectation Maximization algorithm

• Reinforcement learning

Prerequisites:

Basic probability and statistics theory, linear algebra.

Literature:

Machine Learning – A Probabilistic Perspective, Kevin P. Murphy, The MIT Press, 2012

Introduction to Machine Learning – second edition, Ethem Alpaydin, The MIT Press, 2009

Pattern Recognition and Machine Learning, Chris Bishop, Springer, 2006

Pattern Classification, Second Edition, Richard O. Duda, Peter E. Hart, David G. Stork, Wiley Interscience, 2001.

Organizer: Associate Professor Zheng-Hua Tan (zt@es.aau.dk)

Lecturers: Zheng-Hua Tan, Aalborg University, Denmark, zt@es.aau.dk, http://kom.aau.dk/~zt/

ECTS: 3

Time: 7, 9, 13, 20 and 23 April 2015; every day from 9:00-16:00.

Place: Aalborg University

Zip code: 9220

City: Aalborg

Number of seats: 30

Deadline: 17 March, 2015

This course will give a comprehensive introduction to machine learning both by presenting technologies proven valuable and by addressing specific problems such as pattern recognition and data mining. This course covers both theory and practices for machine learning, but with an emphasis on the practical side namely how to effectively apply machine learning to a variety of problems. Topics will include

• Supervised learning (of classification and regression functions)

K-nearest neighbors, decision trees, naïve Bayes, support vector machines, logistic regression, evolutionary algorithms, Bayesian Networks, hidden Markov model, neural networks, boosting

• Unsupervised learning and clustering

K-means, hierarchical clustering (agglomerative and divisive), principal component analysis, Expectation Maximization algorithm

• Reinforcement learning

Prerequisites:

Basic probability and statistics theory, linear algebra.

Literature:

Machine Learning – A Probabilistic Perspective, Kevin P. Murphy, The MIT Press, 2012

Introduction to Machine Learning – second edition, Ethem Alpaydin, The MIT Press, 2009

Pattern Recognition and Machine Learning, Chris Bishop, Springer, 2006

Pattern Classification, Second Edition, Richard O. Duda, Peter E. Hart, David G. Stork, Wiley Interscience, 2001.

Organizer: Associate Professor Zheng-Hua Tan (zt@es.aau.dk)

Lecturers: Zheng-Hua Tan, Aalborg University, Denmark, zt@es.aau.dk, http://kom.aau.dk/~zt/

ECTS: 3

Time: 7, 9, 13, 20 and 23 April 2015; every day from 9:00-16:00.

Place: Aalborg University

Zip code: 9220

City: Aalborg

Number of seats: 30

Deadline: 17 March, 2015

- Teacher: Zheng-Hua Tan