Search result: Catalogue data in Spring Semester 2025
Electrical Engineering and Information Technology Bachelor  | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
 ElectivesThis is only a short selection. Other courses from the ETH course catalogue may be chosen. Please consult the "Richtlinien zu Projekten, Praktika, Seminare" (German only), Link. | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Man-Technology-Environment Electives ("MTU") | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Number | Title | Type | ECTS | Hours | Lecturers | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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| 151-0228-00L | Management & Sustainability of Air Transport | W | 4 credits | 3G | P. Wild, R. McKenna | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract | The lecture provides a comprehensive overview of management, sustainability, planning, processes, and operations in aviation, equipping students with the skills to manage and lead an aeronautical division. Moreover, the modules offer many interdisciplinary insights offering a condensed "Mini MBA". While it is beneficial to have completed "Basics of Air Transport," it is not a prerequisite. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | Upon completing the course, participants will be well-versed in tasks, processes, and interactions, and will possess the ability to comprehend the implications of developments within the airline industry and its surroundings. This knowledge will equip them to effectively operate within the air transport sector. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Content | Overall concept: This lecture builds on the content of lecture "Basics of Air Transport" (151-0227-00L) and provides deeper insights into the airline industry and managment practises. The lecture is taught by svereal different experts from Lufthansa, SWISS, and Federal Office of Civil Aviation. Weekly: 1h independent preparation; 2h lectures and 1 h exercises with an expert in the respective field Content: Strategy, Alliances & Joint Ventures, Sustainable Aviation, Environmental Protection, Safety & Risk Management, Airline Economics, Network Management, Revenue Management & Pricing, Sales & Distribution, Airline Marketing, Scheduling & Slot Management, Fleet Management & Leasing, Continuing Airworthiness Management, Supply Chain Management, Operational Steering. Excursion: We plan an excursion to the freight terminals at Zurich Airport and visits at SWISS HQ, Dispatch, Network Operations Control and Dispo. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Lecture notes | No offical lecture notes. Lecturers' slides will be made available | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Literature | Literature will be provided by the lecturers respective there will be additional information upon registration | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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| 363-1191-00L | #AI4Impact: Machine Learning for Social Impact  Does not take place this semester. | W | 3 credits | 2G | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract | How can AI be leveraged to make real-world impact? This course will introduce students to the fundamentals of machine learning (ML) in a hands-on manner with a focus on applying them to address challenges that will impact people's lives in areas such as health, education, legal, and the UN Sustainable Development Goals more broadly. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | This course seeks to introduce students without prior machine learning (ML) experience backgrounds to the fundamentals of ML and give them hands-on skills to apply ML to solve problems that make real-world impact. Students will learn machine learning concepts such as classification, regression, deep learning, natural language processing, and generative AI, and apply them to real-world datasets in hands-on labs. Furthermore, students will learn to work together in teams to develop ML systems that make real-world impact. After taking this course, students will be able to explore and preprocess data, engineer and select relevant features, train relevant ML models, and conduct thorough experiments to evaluate model performance using appropriate metrics. The hope is that learners will leave the course adequately equipped and inspired to use their newly acquired ML superpowers to make the world a much better place! | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Content | This course seeks to introduce students without prior machine learning (ML) experience backgrounds to the fundamentals of ML and give them hands-on skills to apply ML to solve problems that make real-world impact. Programming experience in Python is a requirement. No prior experience with machine learning is required. The course is structured in lectures, hands-on coding exercises, assignments, and course projects. Lectures In the lectures, the students will be introduced to the fundamentals of ML along with relevant applications. Various topics include algorithms for classification, regression, deep learning, natural language processing, generative AI, and ML pipelines consisting of data exploration and preprocessing, feature extraction and engineering, model training, and evaluation. Lectures will include in-class coding exercises and discussions. It will also feature guest lecturers (e.g., practitioners) who will give talks on ML systems that they have developed and deployed for impact. Assignments Students will work individually to apply the ML concepts introduced in the lectures on provided datasets for impact. Emphasis will be placed on facilitating an intuitive and hands-on understanding of ML models and how to make them work on messy real-world datasets and contexts. Course Projects The course project will put everything together and will be the key deliverable. Students will work collaboratively in teams to implement an ML system for social impact, write a paper on the work with the caliber to be accepted at an applied ML conference in the relevant domain, and present it. Students will be responsible for finding relevant datasets for use. We will explore collaborations with NGOs and companies to make available relevant datasets to use for the project. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Prerequisites / Notice | (1) Programming experience in Python (2) Passionate about making social impact with technology | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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