040514 UE Python for Finance II (MA) (2023S)
Prüfungsimmanente Lehrveranstaltung
Labels
An/Abmeldung
Hinweis: Ihr Anmeldezeitpunkt innerhalb der Frist hat keine Auswirkungen auf die Platzvergabe (kein "first come, first served").
- Anmeldung von Mo 13.02.2023 09:00 bis Mi 22.02.2023 12:00
- Anmeldung von Mo 27.02.2023 09:00 bis Di 28.02.2023 12:00
- Abmeldung bis Fr 17.03.2023 23:59
Details
max. 35 Teilnehmer*innen
Sprache: Englisch
Lehrende
Termine (iCal) - nächster Termin ist mit N markiert
- Dienstag 02.05. 15:00 - 16:30 PC-Seminarraum 5 Oskar-Morgenstern-Platz 1 1.Untergeschoß
- Dienstag 09.05. 13:15 - 16:30 PC-Seminarraum 5 Oskar-Morgenstern-Platz 1 1.Untergeschoß
- Dienstag 16.05. 13:15 - 16:30 PC-Seminarraum 5 Oskar-Morgenstern-Platz 1 1.Untergeschoß
- Dienstag 23.05. 13:15 - 16:30 PC-Seminarraum 5 Oskar-Morgenstern-Platz 1 1.Untergeschoß
- Dienstag 06.06. 13:15 - 16:30 PC-Seminarraum 5 Oskar-Morgenstern-Platz 1 1.Untergeschoß
- Dienstag 13.06. 13:15 - 16:30 PC-Seminarraum 5 Oskar-Morgenstern-Platz 1 1.Untergeschoß
- Dienstag 20.06. 13:15 - 16:30 PC-Seminarraum 5 Oskar-Morgenstern-Platz 1 1.Untergeschoß
- Dienstag 27.06. 13:15 - 16:30 PC-Seminarraum 5 Oskar-Morgenstern-Platz 1 1.Untergeschoß
Information
Ziele, Inhalte und Methode der Lehrveranstaltung
The course enables participants to gain further experience in Python and its applications in Finance. It is expected that students have prior knowledge of Python equivalent to the contents of Python for Finance I. Participants get to know and apply methods from machine learning and natural language processing, with a focus on practical applications of these methods. Students learn to gather textual data from different internet sources, clean the data, and process the data to produce quantitative measures that might be relevant for a task (e.g. an investment strategy). Subsequently, this data and other standard finance data sources are used as inputs to machine learning methods. Besides these specific methods, students will also gain further general knowledge with respect to Python programming and managing a programming project.
Art der Leistungskontrolle und erlaubte Hilfsmittel
The grade will be based on homework exercises that participants are expected to present in class, class participation, and a course project in which students apply the methods learnt in the course.
Mindestanforderungen und Beurteilungsmaßstab
60% homework exercises
10% class participation
30% course projectMinimum requirement for a positive grade: a total of 50%.
10% class participation
30% course projectMinimum requirement for a positive grade: a total of 50%.
Prüfungsstoff
All material covered in class.
Literatur
Bird, S., Klein, E., Loper, E., Natural Language Processing with Python, 2019. https://www.nltk.org/book/Géron, A., Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems, 1st (2017), 2nd (2019), or 3rd (2022) edition. O'Reilly Media.
or
Géron, A., Praxiseinstieg Machine Learning mit Scikit-Learn, Keras und TensorFlow: Konzepte, Tools und Techniken für intelligente Systeme. 2nd edition (2020). O'Reilly Media.Raschka, S., Mirjalili, V., Python Machine Learning: Machine Learning and Deep Learning with Python, scikit-learn, and TensorFlow, 2nd (2017) or 3rd (2019) edition. Packt Publishing.
or
Raschka, S., Mirjalili, V., Machine Learning mit Python und Scikit-Learn und TensorFlow : das umfassende Praxis-Handbuch für Data Science, Predictive Analytics und Deep Learning, 2. Auflage, 2018, or 3. Auflage, 2021. MITP.
or
Raschka, S., Mirjalili, V., Liu, Y. (H.), Machine Learning with PyTorch and Scikit-Learn: Develop machine learning and deep learning models with Python. 1st edition (2022). Packt Publishing.Zatloukal, K., ML & Investing Part 1: From Linear Regression to Ensembles of Decision Stumps, 2018. https://www.osam.com/Commentary/ml-investing-linear-regression-to-decision-stumpsZatloukal, K., ML & Investing Part 2: Clustering, 2019. https://osam.com/pdfs/research/ML-and-Investing-Part-2-Clustering.pdfAQR Capital Management, Can Machines "Learn" Finance? 2019. https://images.aqr.com/-/media/AQR/Documents/Alternative-Thinking/AQR-Alternative-Thinking-2Q19-Can-Machines-Learn-Finance.pdf
or
Géron, A., Praxiseinstieg Machine Learning mit Scikit-Learn, Keras und TensorFlow: Konzepte, Tools und Techniken für intelligente Systeme. 2nd edition (2020). O'Reilly Media.Raschka, S., Mirjalili, V., Python Machine Learning: Machine Learning and Deep Learning with Python, scikit-learn, and TensorFlow, 2nd (2017) or 3rd (2019) edition. Packt Publishing.
or
Raschka, S., Mirjalili, V., Machine Learning mit Python und Scikit-Learn und TensorFlow : das umfassende Praxis-Handbuch für Data Science, Predictive Analytics und Deep Learning, 2. Auflage, 2018, or 3. Auflage, 2021. MITP.
or
Raschka, S., Mirjalili, V., Liu, Y. (H.), Machine Learning with PyTorch and Scikit-Learn: Develop machine learning and deep learning models with Python. 1st edition (2022). Packt Publishing.Zatloukal, K., ML & Investing Part 1: From Linear Regression to Ensembles of Decision Stumps, 2018. https://www.osam.com/Commentary/ml-investing-linear-regression-to-decision-stumpsZatloukal, K., ML & Investing Part 2: Clustering, 2019. https://osam.com/pdfs/research/ML-and-Investing-Part-2-Clustering.pdfAQR Capital Management, Can Machines "Learn" Finance? 2019. https://images.aqr.com/-/media/AQR/Documents/Alternative-Thinking/AQR-Alternative-Thinking-2Q19-Can-Machines-Learn-Finance.pdf
Zuordnung im Vorlesungsverzeichnis
Letzte Änderung: Mo 08.05.2023 15:27