# 401-3620-19L Student Seminar in Statistics: Adversarial and Robust Machine Learning

Semester | Spring Semester 2019 |

Lecturers | P. L. Bühlmann, M. H. Maathuis, N. Meinshausen, S. van de Geer |

Periodicity | every semester recurring course |

Language of instruction | English |

Comment | Number of participants limited to 22. Mainly for students from the Mathematics Bachelor and Master Programmes who, in addition to the introductory course unit 401-2604-00L Probability and Statistics, have heard at least one core or elective course in statistics. Also offered in the Master Programmes Statistics resp. Data Science. |

### Courses

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

401-3620-00 S | Student Seminar in Statistics: Adversarial and Robust Machine Learning | 2 hrs |
| P. L. Bühlmann, M. H. Maathuis, N. Meinshausen, S. van de Geer |

### Catalogue data

Abstract | As statistical and machine learning models are increasingly employed in many real-world applications it becomes more important to understand the vulnerabilities and robustness properties of these models. In the first part of this seminar, we will study papers relating to adversarial examples. In the second part of the course, we will review other types of distribution shifts. |

Objective | After this seminar, you should know - properties of adversarial examples - some attacks and defenses - some concepts from robust optimization and distributional robustness - other distribution shifts that can fool machine learning models in general and neural networks in particular |

Content | As statistical and machine learning models are increasingly employed in many real-world applications it becomes more important to understand the vulnerabilities and robustness properties of these models. In the first part of this seminar, we will study papers relating to adversarial examples, covering their properties, various attacks and defenses. In the second part of the course, we will review other types of distribution shifts, posing significant challenges for state-of-the-art machine learning models. Some parts of the seminar will be devoted to implementing these methods in python. |

Prerequisites / Notice | We require at least one course in statistics or machine learning and basic knowledge in computer programming. Some background knowledge in deep learning is helpful but not strictly required. Topics will be assigned during the first meeting. |

### Performance assessment

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

Performance assessment as a semester course | |

ECTS credits | 4 credits |

Examiners | P. L. Bühlmann |

Type | ungraded semester performance |

Language of examination | English |

Repetition | Repetition only possible after re-enrolling for the course unit. |

### Learning materials

No public learning materials available. | |

Only public learning materials are listed. |

### Groups

No information on groups available. |

### Restrictions

Places | 22 at the most |

Priority | Registration for the course unit is until 29.01.2019 only possible for the primary target group |

Primary target group | Data Science MSc (261000)
Mathematics BSc (404000) starting semester 05 Statistics MSc (436000) Mathematics MSc (437000) Applied Mathematics MSc (437100) Mathematics (Mobility) (448000) |

Waiting list | until 18.02.2019 |

### Offered in

Programme | Section | Type | |
---|---|---|---|

Data Science Master | Seminar | W | |

Mathematics Bachelor | Seminars | W | |

Mathematics Master | Seminars | W | |

Statistics Master | Seminar or Semester Paper | W |