Dennis Komm: Catalogue data in Autumn Semester 2019

Name Prof. Dr. Dennis Komm
FieldAlgorithms and Didactics
Address
Professur Algorithmen und Didaktik
ETH Zürich, CAB F 10.1
Universitätstrasse 6
8092 Zürich
SWITZERLAND
Telephone+41 44 632 82 23
E-maildennis.komm@inf.ethz.ch
URLhttps://people.inf.ethz.ch/dkomm/
DepartmentComputer Science
RelationshipAssociate Professor

NumberTitleECTSHoursLecturers
252-0852-00LFoundations of Computer Science Information 4 credits2V + 2UL. E. Fässler, M. Dahinden, D. Komm
AbstractStudents learn to apply selected concepts and tools from computer science for working on interdisciplinary projects.

The following topics are covered: modeling and simulations, introduction to programming, visualizing multi-dimensional data, introduction matrices, managing data with lists and tables and with relational databases, universal methods for algorithm design.
Learning objectiveThe students learn to

- understand the role of computer science in science,
- to control computer and automate processes of problem solving by programming,
- choose and apply appropriate tools from computer science,
- process and analyze real-world data from their subject of study,
- handle the complexity of real-world data.
Content1. The role of computer science in science
2. Introduction to Programming with Python
3. Modeling and simulations
4. Introduction to Matrices with Matlab
5. Visualizing multidimensional data
6. Data management with lists and tables
7. Data management with a relational database
Lecture notesAll materials for the lecture are available at www.gdi.ethz.ch
LiteratureL. Fässler, M. Dahinden, D. Komm, and D. Sichau: Einführung in die Programmierung mit Python und Matlab. Begleitunterlagen zum Onlinekurs und zur Vorlesung, 2016. ISBN: 978-3741250842.
L. Fässler, M. Dahinden, and D. Sichau: Verwaltung und Analyse digitaler Daten in der Wissenschaft. Begleitunterlagen zum Onlinekurs und zur Vorlesung, 2017.
Prerequisites / NoticeThis course is based on application-oriented learning. The students spend most of their time working through projects with data from natural science and discussing their results with teaching assistants. To learn the computer science basics there are electronic tutorials available.
252-0866-00LDigital Medicine I: Introduction to Programming Information Restricted registration - show details
Only for Human Medicine BSc
2 credits2GH.‑J. Böckenhauer, D. Komm
AbstractThis lecture gives an introduction to programming in Python and an overview of basic problem solving strategies and design principles for efficient algorithms and data structures.
Learning objectiveTo learn basic principles of programming in Python and to apply them for implementing algorithmic approaches for solving simple computational problems.
ContentThis lecture has two goals. On the one hand, an introduction to programming is given, using Python as a sample language. This introduction includes the basic programming principles such as truth values, variables, data types, conditional statements, loops, and functions.

On the other hand, basic data structures (like stacks, queues, or search trees) and important concepts of algorithm design are presented and implemented in Python to efficiently solve basic algorithmic tasks on these data structures.

The main focus lies on general-purpose design techniques for efficient algorithms, such as the greedy method, dynamic programming, or the divide and conquer strategy. These techniques are demonstrated with many examples from practice.
Lecture notesAll learning materials will be provided during the course.