Marcel Lüthi: Catalogue data in Autumn Semester 2024 |
| Name | Dr. Marcel Lüthi |
| Address | Lehre D-INFK ETH Zürich, CAB H 39.2 Universitätstrasse 6 8092 Zürich SWITZERLAND |
| Telephone | +41 44 632 78 69 |
| marcel.luethi@inf.ethz.ch | |
| Department | Computer Science |
| Relationship | Lecturer |
| Number | Title | ECTS | Hours | Lecturers | |||||||||||||||||||||||||||||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 252-0232-00L | Software Engineering   | 6 credits | 2V + 2U | M. Lüthi, M. Schwerhoff, H. Lehner | |||||||||||||||||||||||||||||||||||||||||
| Abstract | This course introduces both theoretical and practical aspects of software engineering, all of which are applied in a substantial team project. | ||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | The course has two main objectives: - Obtain an end-to-end (both, theoretical and practical) understanding of the core techniques used for building quality software. - Be able to apply these techniques in practice. | ||||||||||||||||||||||||||||||||||||||||||||
| Content | This course introduces theoretical and applied aspects of software engineering, including: requirements, specifications and documentation, formal and informal modelling, modularity, and testing and concolic execution. The theoretical foundations provided in the lecture, from understanding requirements over design and implementation to deployment and change requests, will be applied by the students in a mandatory project that spans the whole semester: developing, as a team, a small multiplayer game with graphical user interface and network support. Lectures and project use C++, and we expect knowledge corresponding to lecture 252-0856 Computer Science. | ||||||||||||||||||||||||||||||||||||||||||||
| Lecture notes | no lecture notes | ||||||||||||||||||||||||||||||||||||||||||||
| Literature | Will be announced in the lecture | ||||||||||||||||||||||||||||||||||||||||||||
| Competencies |
| ||||||||||||||||||||||||||||||||||||||||||||
| 252-0845-00L | Computer Science I | 5 credits | 2V + 2U | M. Lüthi, A. Streich | |||||||||||||||||||||||||||||||||||||||||
| Abstract | The course covers the basic concepts of computer programming. | ||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | Basic understanding of programming concepts. Students will be able to write and read simple programs and to modify existing programs. In the course "Computer Science I", the competency of programming is taught, applied and examined. Furthermore modeling is taught and applied. | ||||||||||||||||||||||||||||||||||||||||||||
| Content | variables, types, control structures, functions, scoping, recursion, object-oriented programming. The programming language is Python. | ||||||||||||||||||||||||||||||||||||||||||||
| Lecture notes | The slides and lecture notes will be made available for download on the course website. | ||||||||||||||||||||||||||||||||||||||||||||
| Literature | Learn to Code by Solving Problems A Python Programming Primer Daniel Zingaro Python Crash Course A Hands-On, Project-Based Introduction to Programming Eric Matthes Python for Data Analysis Data wrangling with pandas, NumPy & Jupyter, 3rd Edition Wes McKinney | ||||||||||||||||||||||||||||||||||||||||||||
| Competencies |
| ||||||||||||||||||||||||||||||||||||||||||||
| 252-0846-AAL | Computer Science II Enrolment ONLY for MSc students with a decree declaring this course unit as an additional admission requirement. Any other students (e.g. incoming exchange students, doctoral students) CANNOT enrol for this course unit. | 4 credits | 9R | M. Fischer, M. Lüthi | |||||||||||||||||||||||||||||||||||||||||
| Abstract | Introduction to programming in Java. Procedural foundations of programming and outlook to object oriented programming. Variables, types, assignments, control structures (branch, loop), data structures, algorithms, line graphics, graphical user interface. Writing small programs. Working with a professional programming environment (Eclipse). | ||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | In the course "Computer Science II", the competencies of programming, modeling and data analysis & interpretation are taught, applied and examined. The students will be able to write simple programs and to modify existing programs. | ||||||||||||||||||||||||||||||||||||||||||||
| Content | This course offers an introduction to variables, control structures (branch, loop), algorithms and data structures, as well as an outlook to modularisation and object oriented techniques. In the exercises students train programming skills (in the programming language JAVA). Students can solve the exercises on their own laptop or in the computer labs at ETH. The software used in this course runs on MS Windows, MacOS X and Linux. | ||||||||||||||||||||||||||||||||||||||||||||
| Prerequisites / Notice | Prerequisites: 252-0845-00 Computer Science I (D-BAUG) | ||||||||||||||||||||||||||||||||||||||||||||
| 275-0003-00L | Data Science & Machine Learning | 4 credits | 3V | M. Lüthi, A. Streich | |||||||||||||||||||||||||||||||||||||||||
| Abstract | This course provides a fundamental training in the areas of data science and machine learning. It is intended for managers and leaders who want to understand the typical workflow, fundamental techniques and key challenges of data science and machine learning to drive successful implementations. | ||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | After taking this course the participants - have a good understanding of the basic methods of data science and machine learning - know the typical data science workflow and can understand and assess the role and importance of each individual step - understand the importance of quantifying and communicating uncertainty in the data - know the importance and basic techniques of cleaning and organizing data and can perform simple data cleaning tasks in pandas. - can identify suitable algorithms and select the best-suited one for a given task - can apply machine learning methods as implemented in scikit-learn on tabular data - understand the basic ideas behind modern deep learning methods and can implement simple deep learning models in tensorflow - understand some key applications such as natural language processing or computer vision. - are able to apply the learned methods to practical problems in data science. | ||||||||||||||||||||||||||||||||||||||||||||
| Content | We will cover the following topics - The typical data science workflow - Cleaning, organizing and preparing data for further analysis - Exploratory data analysis: Gaining an understanding through visualizing and summarizing data - Basics of statistical inference and uncertainty quantification - Correlations and regression. - Basics of Machine learning, including supervised and unsupervised learning, model evaluation and model selection - Standard algorithms such as linear regression, decision trees, k-nearest neighbors, k-means, principal component analysis - Identification of the best-suited algorithm and models for a given dataset and machine learning task - Foundations of Deep Learning - Challenges & Considerations: Potential pitfalls, threats, and ethical considerations. The theoretical parts will be complemented by practical exercises using python, pandas, numpy, matplotlib, scikit-learn, and tensorflow. | ||||||||||||||||||||||||||||||||||||||||||||
| Competencies |
| ||||||||||||||||||||||||||||||||||||||||||||