Machine Learning (2016)

  • 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: 9:00-16:00, April 29, May 2, 4, 9, 13, 2016. 

  • Place: Fredrik Bajers Vej 7A/4-106, 9220 Aalborg, Aalborg University

  • Zip code: 9220

  • City: Aalborg

  • Number of seats: 30

  • Deadline: 8 April, 2016

  • Important information concerning PhD courses: 

    We have over some time experienced problems with no-show for both project and general courses. It has now reached a point where we are forced to take action. Therefore, the Doctoral School has decided to introduce a no-show fee of DKK 5,000 for each course where the student does not show up. Cancellations are accepted no later than 2 weeks before start of the course. Registered illness is of course an acceptable reason for not showing up on those days. Furthermore, all courses open for registration approximately three months before start. This can hopefully also provide new students a chance to register for courses during the year. We look forward to your registrations.