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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


Teilnahmebedingungen *) 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 wöchentlich
Datum 04.03.2021 – 15.04.2021
Zeit Donnerstag, 14.15-18.00 - Online Präsenz -

The course will be taught online at the dates you can see below:

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)
Leistungsüberprüfung Semesterendprüfung
Hinweise zur Leistungsüberprüfung Combination of active participation, assignment(s) and final exam.
written exam: 29.4.21; 14:15-15:00. Electronic exam.
An-/Abmeldung zur Leistungsüberprüfung Belegen via MOnA innerhalb der Belegfrist
Wiederholungsprüfung keine Wiederholungsprüfung
Skala 1-6 0,1
Wiederholtes Belegen beliebig wiederholbar
Zuständige Fakultät Wirtschaftswissenschaftliche Fakultät / WWZ, studiendekanat-wwz@unibas.ch
Anbietende Organisationseinheit Wirtschaftswissenschaftliche Fakultät / WWZ