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10616-01 - Vorlesung: Machine Learning (3 KP)
| Semester | Frühjahrsemester 2021 |
| Angebotsmuster | Jedes Frühjahrsem. |
| Dozierende | Dietmar Maringer (dietmar.maringer@unibas.ch, BeurteilerIn) |
| Inhalt | To counter-act the "data-rich, information-poor" ("drip") syndrome, this course covers concepts and techniques that aim at explorative analysis: finding structure within data, and, ideally, extracting information. Methods include (but are not limited to) non-linear regression, perceptrons and neural networks, support vector machines, and tree-based, kernel-based, or rule-based methods. Typical applications are classification, prediction, clustering, or dimension reduction. Theoretical presentations are complemented with hands-on examples using R and Python. Special emphasis will be given to validation and model selection. Time permitting, we will also discuss issues such as data preprocessing and data management. |
| Lernziele | Solid understanding of key machine learning techniques, their advantages and limitations, and application skills. |
| Literatur | 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, 2nd ed., MIT Press 2010. *) 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. *) 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, 3rd ed., Elsevier 2011. Specific recommendations and additional literature to be announced during the course. |
| Weblink | Weblink on ADAM |
| Teilnahmevoraussetzungen | *) completed BA in Business and Economics *) 12036 Econometrics *) Basic programming skills (R and/or Python) |
| Anmeldung zur Lehrveranstaltung | Registration: Please enrol in MOnA. EUCOR-Students and students of other Swiss Universities have to enrol at the students administration office (studseksupport1@unibas.ch) within the official enrolment period. Enrolment = Registration for the exam! |
| Unterrichtssprache | Englisch |
| Einsatz digitaler Medien | Online-Veranstaltung |
| Intervall | Wochentag | Zeit | Raum |
|---|---|---|---|
| wöchentlich | Donnerstag | 14.15-18.00 | - Online Präsenz - |
| Bemerkungen | The course will be taught online at the dates you can see below: |
Einzeltermine
| Datum | Zeit | Raum |
|---|---|---|
| Donnerstag 04.03.2021 | 14.15-18.00 Uhr | - Online Präsenz -, -- |
| Donnerstag 11.03.2021 | 14.15-18.00 Uhr | - Online Präsenz -, -- |
| Donnerstag 18.03.2021 | 14.15-18.00 Uhr | - Online Präsenz -, -- |
| Donnerstag 25.03.2021 | 14.15-18.00 Uhr | - Online Präsenz -, -- |
| Donnerstag 01.04.2021 | 14.15-18.00 Uhr | Ostern |
| Donnerstag 08.04.2021 | 14.15-18.00 Uhr | - Online Präsenz -, -- |
| Donnerstag 15.04.2021 | 14.15-18.00 Uhr | - Online Präsenz -, -- |
| Module |
Modul: Kernbereich Wirtschaftswissenschaften (Masterstudium: Sustainable Development) Vertiefungsmodul: Marketing and Strategic Management (Masterstudium: Wirtschaftswissenschaften) Vertiefungsmodul: Quantitative Methods (Masterstudium: Wirtschaftswissenschaften) |
| Prüfung | Semesterendprüfung |
| Hinweise zur Prüfung | Combination of active participation, assignment(s) and final exam. written exam: 29.4.21; 14:15-15:00. Electronic exam. |
| An-/Abmeldung zur Prüfung | Anmeldung: Belegen |
| Wiederholungsprüfung | keine Wiederholungsprüfung |
| Skala | 1-6 0,1 |
| Belegen bei Nichtbestehen | beliebig wiederholbar |
| Zuständige Fakultät | Wirtschaftswissenschaftliche Fakultät / WWZ, studiendekanat-wwz@unibas.ch |
| Anbietende Organisationseinheit | Wirtschaftswissenschaftliche Fakultät / WWZ |