Computer Science - Machine Learning

 

General information

Course name Machine Learning
Course type Lecture
Course code Inf-MaLearn
Course coordinator Prof. Dr. Carsten Meyer
Faculty Engineering
Examination office pruefungsamt@informatik.uni-kiel.de
Short summary Machine learning (a branch of artificial intelligence) is concerned with the design and development of algorithms that allow technical systems to solve tasks and to improve their performance by ("clever") learning from examples. The aim of this course is to provide a fundamental understanding of important concepts in machine learning, both from a theoretical and an application point of view. Several learning tasks (classification, regression, clustering), learning modes (supervised learning, unsupervised learning, reinforcement learning) and learning machines (support vector machine, perceptron, decision tree) are covered, in addition to methods for dimensionality reduction (principal component analysis, linear discriminant analysis) and algorithms for model selection and model combination (bagging, boosting).
   

Information about study level

Study level Master
Also possible for  
   

Information about credit points, evaluation and frequency

ECTS 6
Evaluation Oral exam
Frequency Summer term
   

Information about teaching language

Teaching language English
Minimum language requirement B1
Further information on the teaching language  
   

Information about requirements

Recommended requirements Mathematical basics of algebra and analysis and of optimization.
   

Information about course content, reading list and additional information

Course Content The following aspects will be covered in the lecture:
  1. Introduction, Machine learning basics
  2. Supervised classification: Support vector machines, decision trees, perceptron
  3. Unsupervised learning / clustering
  4. Dimensionality reduction: Linear discriminant analysis, principal component analysis
  5. Model selection
  6. Ensemble methods: Bagging, boosting
  7. Reinforcement learning
The exercises contain theoretical and practical exercises (based on available software libraries written in the python programming language) to deepen the understanding of the algorithms.
Reading list T. Mitchell, "Machine learning", McGraw Hill, 1997 E. Alpaydin, "Introduction to Machine Learning", MIT Press, 2010 S. Marsland, "Machine Learning: An Algorithmic Perspective", CRC Press, 2009 C. M. Bishop, "Pattern recognition and Machine learning", Springer, 2006 R. Duda et al., "Pattern classification", Wiley, 2001 S. Haykin, "Neural networks and learning machines", Prentice Hall, 2008
Additional information