263-4508-00L Algorithmic Foundations of Data Science
| Semester | Spring Semester 2023 |
| Lecturers | D. Steurer |
| Periodicity | yearly recurring course |
| Language of instruction | English |
Courses
| Number | Title | Hours | Lecturers | |||||||
|---|---|---|---|---|---|---|---|---|---|---|
| 263-4508-00 V | Algorithmic Foundations of Data Science | 3 hrs |
| D. Steurer | ||||||
| 263-4508-00 U | Algorithmic Foundations of Data Science | 2 hrs |
| D. Steurer | ||||||
| 263-4508-00 A | Algorithmic Foundations of Data Science | 4 hrs | D. Steurer |
Catalogue data
| Abstract | This course provides rigorous theoretical foundations for the design and mathematical analysis of efficient algorithms that can solve fundamental tasks relevant to data science. |
| Learning objective | We consider various statistical models for basic data-analytical tasks, e.g., (sparse) linear regression, principal component analysis, matrix completion, community detection, and clustering. Our goal is to design efficient (polynomial-time) algorithms that achieve the strongest possible (statistical) guarantees for these models. Toward this goal we learn about a wide range of mathematical techniques from convex optimization, linear algebra (especially, spectral theory and tensors), and high-dimensional statistics. We also incorporate adversarial (worst-case) components into our models as a way to reason about robustness guarantees for the algorithms we design. |
| Content | Strengths and limitations of efficient algorithms in (robust) statistical models for the following (tentative) list of data analysis tasks: - (sparse) linear regression - principal component analysis and matrix completion - clustering and Gaussian mixture models - community detection |
| Lecture notes | To be provided during the semester |
| Literature | High-Dimensional Statistics A Non-Asymptotic Viewpoint by Martin J. Wainwright |
| Prerequisites / Notice | Mathematical and algorithmic maturity at least at the level of the course "Algorithms, Probability, and Computing". Important: Optimization for Data Science 2018--2021 This course was created after a reorganization of the course "Optimization for Data Science" (ODS). A significant portion of the material for this course has previously been taught as part of ODS. Consequently, it is not possible to earn credit points for both this course and ODS as offered in 2018--2021. This restriction does not apply to ODS offered in 2022 or afterwards and you can earn credit points for both courses in this case. |
Performance assessment
| Performance assessment information (valid until the course unit is held again) | |
Performance assessment as a semester course | |
| ECTS credits | 10 credits |
| Examiners | D. Steurer |
| 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 240 minutes |
| Additional information on mode of examination | During the course of the semester, we will assign two graded homeworks as compulsory continuous performance assessments, accounting together for 30% of the final grade (15% for each graded homework). The written session examination accounts for the remaining 70% of the final grade. |
| Written aids | None |
| This information can be updated until the beginning of the semester; information on the examination timetable is binding. | |
Learning materials
| Main link | Course Website |
| Only public learning materials are listed. | |
Groups
| No information on groups available. |
Restrictions
| There are no additional restrictions for the registration. |
