Computational Statistics (Sommersemester 2023)

Lecturer: Prof. Dr. Bernhard Renard (Data Analytics and Computational Statistics) , Fabio Malcher Miranda (Data Analytics and Computational Statistics) , Pauline Hiort (Data Analytics and Computational Statistics) , Hendrik Rätz (Data Analytics and Computational Statistics)
Course Website: https://moodle.hpi.de/course/view.php?id=433

General Information

• Weekly Hours: 4
• Credits: 6
• Graded: yes
• Enrolment Deadline: 01.04.2023 - 07.05.2023
• Examination time §9 (4) BAMA-O: siehe Beschreibung, letzter Termin Rücktritt: 04.06.2023
• Teaching Form: Lecture / Exercise
• Enrolment Type: Compulsory Module
• Course Language: English

Programs, Module Groups & Modules

Description

In almost all areas of life, large amounts of data are generated, requiring dedicated procedures for data analysis to allow predictions and inference for decision making. Computational statistical methods have evolved to cope with challenges arising from large datasets that are not tractable with traditional approaches, e.g. when the number of possible parameters of a model exceeds the number of observations. At the same time, this wealth of data allows replacing distributional assumptions with data-driven analyses.

In this course, we will cover statistical summary of data, hypothesis testing, regression as well as statistical learning approaches with focus on clustering and classification. We will contrast traditional frequentist approaches for these tasks with non-parametric, computational more intensive alternatives and Bayesian approaches.

The lecture will be accompanied by regularly scheduled exercises, which focus on applying the covered method to real-life data from different areas of life. In the course we will work with R / Python. Basic programming knowledge is a prerequisite to successfully complete the exercises. For those students who are not familiar with any of these two languages, an introduction to R will be provided.

Learning Objectives:

• Understand concepts and methods of computational statistics
• Ability to statistically evaluate real-world data
• Ability to assess the quality and validity of a statistical method for a given analysis
• Ability to select, implement and apply appropriate statistical methods and algorithms for a given use case

Requirements

• Fundamentals in calculus and vector analysis (at least comparable to the Mathematik I + II lectures in the ITSE Bachelor at HPI, Mathematik III is a plus)
• Basic programming knowledge (Python or R are a plus)
• Knowledge of English (The lecture will be given in English, but you can ask questions in German and submit German solutions etc.)

Literature

1. Hastie, Trevor ; Tibshirani, Robert ; Friedman, Jerome: The elements of statistical learning: data mining, inference and prediction. 2 : Springer, 2009 (https://web.stanford.edu/~hastie/ElemStatLearn/)
2. James, Gareth ; Witten, Daniela ; Hastie, Trevor ; Tibshirani, Robert: An Introduction to Statistical Learning -- with Applications in R. 103. New York : Springer, 2013 (Springer Texts in Statistics). - ISBN 978-1-4614-7137-0 (http://faculty.marshall.usc.edu/gareth-james/ISL/)

Learning

Lectures and Exercises

Examination

Final exam covering all lecture materials (70% of final grade)

1 graded mid-semester exams (30% of the final grade),

Weekly to biweekly exercises (ungraded)

Students are not required to hand-in exercise solutions but need to present selected solutions (appointed 1 week before the presentation).

Dropping the course is possible until May 24^th (1 week before the tentative midterm).

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