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| Semester | spring semester 2024 |
| Course frequency | Every spring sem. |
| Lecturers | Dietmar Maringer (dietmar.maringer@unibas.ch, Assessor) |
| Content | To counteract the "data-rich, information-poor" ("DRIP") syndrome, this course covers concepts for data analysis and techniques for finding structure in data and, ideally, extracting information. Typical applications are classification, clustering and dimension reduction. Methods include nonlinear methods; perceptrons and neural networks; support vector machines; and tree-, kernel- or rule-based methods, 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 2009. *) 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. Specific recommendations and additional literature to be announced during the course. |
| Comments | Throughout the course, we will use Python to implement methods and concepts, and perform experiments. Participants are expected to have at least a basic knowledge of programming as 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 |
| Date | Time | Room |
|---|---|---|
| Thursday 29.02.2024 | 14.15-18.00 | Wirtschaftswissenschaftliche Fakultät, Grosses PC-Labor S18 HG.37 |
| Thursday 07.03.2024 | 14.15-18.00 | Wirtschaftswissenschaftliche Fakultät, Grosses PC-Labor S18 HG.37 |
| Thursday 14.03.2024 | 14.15-18.00 | Wirtschaftswissenschaftliche Fakultät, Grosses PC-Labor S18 HG.37 |
| Thursday 21.03.2024 | 14.15-18.00 | Wirtschaftswissenschaftliche Fakultät, Grosses PC-Labor S18 HG.37 |
| Thursday 28.03.2024 | 14.15-18.00 | Ostern |
| Thursday 04.04.2024 | 14.15-18.00 | Wirtschaftswissenschaftliche Fakultät, Grosses PC-Labor S18 HG.37 |
| Thursday 11.04.2024 | 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) |
| Assessment format | record of achievement |
| Assessment details | Combination of active participation, assignment(s) and final exam. written exam: 30.04.24; 12:30-13:30. WWz S15: A-Z. 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 |