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10616-01 - Lecture: Applied Machine Learning (3 CP)
| Semester | spring semester 2025 |
| Course frequency | Every spring sem. |
| Lecturers | Dietmar Maringer (dietmar.maringer@unibas.ch, Assessor) |
| Content | To address the "data-rich, information-poor" (DRIP) syndrome, this course introduces key concepts in data analysis and techniques for discovering structure in data to effectively extract meaningful information. Typical applications include classification, clustering, and dimensionality reduction. The methods covered span a range of approaches, including nonlinear methods, perceptrons, neural networks, support vector machines, as well as tree-, kernel-, rule-based, and generative methods. In addition to theoretical presentations, numerous practical applications are carried out. Special attention is paid to data preprocessing, model validation, and model selection. |
| Learning objectives | Solid understanding of key machine learning techniques, their advantages and limitations, and application skills. |
| Bibliography | Lecture material will be provided. There is no designated textbook, but quite a few books participants might find helpful. These include (in alphabetical order): *) E. Alpaydin, Introduction to Machine Learning, 4th ed., MIT Press 2020. *) B.S. Everitt and T. Hothorn. An Introduction to Applied Multivariate Analysis with R. Springer, 2011. *) B.S. Everitt, S. Landau, M. Leese, and D. Stahl. Cluster Analysis. Wiley, 2011. *) T. Hastie, R. Tibshirani, J. Friedman, The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd ed., Springer 2013. *) K.P. Murphy, Machine Learning: A Probabilistic Perspective, The MIT Press, 2012. *) A.C. Rencher. Methods of Multivariate Analysis. Wiley, 3rd edition, 2012. *) I.H. Witten, E. Frank, M.A. Hall, Data Mining: Practical Machine Learning Tools and Techniques, 4th ed., Elsevier 2016. *) Jake VanderPlas, Python Data Science Handbook, O'Reilly 2016. *) Amin Zollanvari, Machine Learning with Python, Springer 2023. Specific recommendations and additional literature to be announced during the course. |
| Comments | Throughout the course, we will use Python to implement various methods and concepts, as well as to conduct experiments. Participants are expected to have a basic knowledge of Python programming, at least to the level taught in "58989 Computing for Business and Economics." |
| Weblink | Weblink on ADAM |
| Admission requirements | *) completed BA in Business and Economics *) 12036 Econometrics *) 58989 Computing for Business and Economics or equivalent |
| Course application | Registration: Please enroll in the Online Services (services.unibas.ch); Eucor-Students and mobility students of other Swiss Universities or the FHNW first have to register at the University of Basel BEFORE the start of the course and receive their login data by post (e-mail address of the University of Basel). Processing time up to a week! Detailed information can be found here: https://www.unibas.ch/de/Studium/Mobilitaet.html After successful registration you can enroll for the course in the Online Services (services.unibas.ch). Applies to everyone: Enrolment = Registration for the course and the exam! |
| Language of instruction | English |
| Use of digital media | No specific media used |
| Interval | Weekday | Time | Room |
|---|---|---|---|
| wöchentlich | Thursday | 14.15-18.00 | Wirtschaftswissenschaftliche Fakultät, Grosses PC-Labor S18 HG.37 |
Dates
| Date | Time | Room |
|---|---|---|
| Thursday 20.02.2025 | 14.15-18.00 | Wirtschaftswissenschaftliche Fakultät, Grosses PC-Labor S18 HG.37 |
| Thursday 27.02.2025 | 14.15-18.00 | Wirtschaftswissenschaftliche Fakultät, Grosses PC-Labor S18 HG.37 |
| Thursday 06.03.2025 | 14.15-18.00 | Wirtschaftswissenschaftliche Fakultät, Grosses PC-Labor S18 HG.37 |
| Thursday 13.03.2025 | 14.15-18.00 | Fasnachstferien |
| Thursday 20.03.2025 | 14.15-18.00 | Wirtschaftswissenschaftliche Fakultät, Grosses PC-Labor S18 HG.37 |
| Thursday 27.03.2025 | 14.15-18.00 | Wirtschaftswissenschaftliche Fakultät, Grosses PC-Labor S18 HG.37 |
| Thursday 03.04.2025 | 14.15-18.00 | Wirtschaftswissenschaftliche Fakultät, Grosses PC-Labor S18 HG.37 |
| Modules |
Module: Core Competences in Economics (Master's Studies: Sustainable Development) Module: Field Electives in Economics and Public Policy (Master's Studies: Economics and Public Policy) Module: Preparation Master's Thesis in Economics (Master's Studies: Sustainable Development) Module: Specific Electives in Data Science and Computational Economics (Master's Studies: Business and Economics) Module: Specific Electives in Marketing and Strategic Management (Master's Studies: Business and Economics) Module: Technology Field (Master's Studies: Business and Technology) Specialization Module: Areas of Specialization in International and/or Monetary Economics (Master's Studies: International and Monetary Economics) |
| Assessment format | record of achievement |
| Assessment details | Combination of active participation, assignment(s) and final exam. written exam: Date and room tbd Late deregistration is not possible for this course. If you do not wish to take part in the exam, please cancel your registration within the registration deadline. |
| Assessment registration/deregistration | Reg.: course registration, dereg: cancel course registration |
| Repeat examination | no repeat examination |
| Scale | 1-6 0,1 |
| Repeated registration | as often as necessary |
| Responsible faculty | Faculty of Business and Economics , studiendekanat-wwz@unibas.ch |
| Offered by | Faculty of Business and Economics |