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# Chair of Mathematical Statistics (SMAT)

# Statistics for Data Science - MATH 413

#### Instructor: Prof. Victor Panaretos

#### Assistants: Anirvan Chakraborty, Valentina Masarotto, Yoav Zemel

#### Announcements

- 13.12.2017: assignment 4 in week 9 and the solution of assignment 1 in week 10 have been updated.

#### Description

Statistics lies at the foundation of data science, providing a unifying theoretical and methodological backbone for the diverse tasks enountered in this emerging field. This course rigorously develops the key notions and methods of statistics, with an emphasis on concepts rather than techniques.

#### Topics include:

- Probability background.
- Entropy and Exponential Families.
- Sampling Theory: information and stochastic convergence.
- Bias and Variance, and the Cramér-Rao bound.
- Likelihood theory.
- Testing and Confidence Regions.
- Nonparametric Estimation and Smoothing.
- Gaussian Linear Regression.
- Generalised Linear Models.
- Nonparametric Regression.

#### Required prior knowledge

Introductory courses in probability and statistics; Basic analysis and linear algebra.

#### Recommended Texts

Davison, A.C. (2003). Statistical Models, Cambridge.

Panaretos, V.M. (2016). Statistics for Mathematicians. Birkhäuser.

Wasserman, L. (2004). All of Statistics. Springer.

Friedman, J., Hastie, T. and Tibshirani, R. (2010). The Elements of Statistical Learning. Springer

#### Exam Information

There will be a mock midterm exam and a written final exam.

The date of the midterm exam is to be determined and will be announced well in advance.

#### Fall 2017 Schedule

Lectures: | CE 5 | Mondays, 12:15-14:00 |
---|---|---|

MA B1 11 | Tuesdays, 14:15-16:00 | |

Exercises: | MA B1 11 | Wednesdays, 08:15-10:00 |