  Cursuscode   INFOMPR 
   
     
After completing this course, the student:
 knows important methods of statistical pattern recognition and their theoretical foundation
 knows general principles of statistical learning, such as overfitting and the biasvariance decomposition
 knows how to apply methods of statistical pattern recognition to practical data analysis problems
 can perform an experimental evaluation of statistical learning methods in a sound manner
 knows the fundamental deep learning topics
 has practical skills for applying deep learning methods (e.g. feed forward neural networks, CNNs, RNNs)
 can model a researchoriented problem in the application domain of deep learning and suggest a solution using deep learning techniques
Assessment
The course is graded through a practical assignment (20% of the final mark), a group project (40%) and a written exam (40%).
A repair test requires at least a 4 for the original test.


In this course we study statistical pattern recognition and machine learning.
The subjects covered are:
 General principles of data analysis: overfitting, biasvariance tradeoff, model selection, regularization, the curse of dimensionality.
 Linear statistical models for regression and classification.
 Clustering and unsupervised learning.
 Support vector machines.
 Neural networks and deep learning.
Knowledge of elementary probability theory, statistics, multivariable calculus and linear algebra is presupposed.
Course form
Lectures and computer lab sessions.
Literature
 Book: Christopher M. Bishop, Pattern Recognition and Machine Learning, Springer, 2006.
 Book: Ian Goodfellow, Yoshua Bengio and Aaron Courville, Deep Learning, MIT Press, 2016.
Book URL: http://www.deeplearningbook.org.
 Possibly additional literature in the form of research papers, book chapters, etcetera.




   

 
 