# 252-0535-00L Machine Learning

Semester | Autumn Semester 2013 |

Lecturers | A. Krause |

Periodicity | yearly recurring course |

Language of instruction | English |

### Catalogue data

Abstract | Machine learning algorithms provide analytical methods to search data sets for characteristic patterns. Typical tasks include the classification of data, function fitting and clustering, with applications in image and speech analysis, bioinformatics and exploratory data analysis. This course is intended as an introduction, advanced topics will be discussed in “Statistical Learning Theory". |

Objective | Students will be familiarized with the most important concepts and algorithms for supervised and unsupervised learning; reinforce the statistics knowledge which is indispensible to solve modeling problems under uncertainty. Key concepts are the generalization ability of algorithms and systematic approaches to regularization. |

Content | The theory of fundamental machine learning concepts is presented in the lecture, and illustrated with relevant applications. Students can deepen their understanding by solving both pen-and-paper and programming exercises, where they implement and apply famous algorithms to real-world data. Topics covered in the lecture include: - Bayesian theory of optimal decisions - Maximum likelihood and Bayesian parameter inference - Classification with discriminant functions: Perceptrons, Fisher's LDA and support vector machines (SVM) - Ensemble methods: Bagging and Boosting - Regression: least squares, ridge and LASSO penalization, non-linear regression and the bias-variance trade-off - Non parametric density estimation: Parzen windows, nearest nieghbour - Dimension reduction: principal component analysis (PCA) and beyond |

Lecture notes | No lecture notes, but slides will be made available on the course webpage. |

Literature | C. Bishop. Pattern Recognition and Machine Learning. Springer 2007. R. Duda, P. Hart, and D. Stork. Pattern Classification. John Wiley & Sons, second edition, 2001. T. Hastie, R. Tibshirani, and J. Friedman. The Elements of Statistical Learning: Data Mining, Inference and Prediction. Springer, 2001. L. Wasserman. All of Statistics: A Concise Course in Statistical Inference. Springer, 2004. |

Prerequisites / Notice | Solid basic knowledge in analysis, statistics and numerical methods for CSE. Familiarity with Matlab for solving the programming exercises. |

### Performance assessment

Performance assessment information (valid until the course unit is held again) | |

Performance assessment as a semester course | |

ECTS credits | 6 credits |

Examiners | A. Krause |

Type | session examination |

Language of examination | English |

Repetition | The performance assessment is only offered in the session after the course unit. Repetition only possible after re-enrolling. |

Mode of examination | written 120 minutes |

Additional information on mode of examination | 70% schriftliche Sessionsprüfung, 30% Projekt |

Written aids | Two A4-pages (i.e. one A4-sheet of paper), either handwritten or 11 point minimum font size. |

This information can be updated until the beginning of the semester; information on the examination timetable is binding. |

### Learning materials

Main link | Information |

Only public learning materials are listed. |

### Courses

Number | Title | Hours | Lecturers | |||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|

252-0535-00 V | Machine Learning | 3 hrs |
| A. Krause | ||||||||||||

252-0535-00 U | Machine Learning | 2 hrs |
| A. Krause |

### Groups

No information on groups available. |

### Restrictions

There are no additional restrictions for the registration. |