040514 UE Python for Finance II (MA) (2023S)
Continuous assessment of course work
Labels
Registration/Deregistration
Note: The time of your registration within the registration period has no effect on the allocation of places (no first come, first served).
- Registration is open from Mo 13.02.2023 09:00 to We 22.02.2023 12:00
- Registration is open from Mo 27.02.2023 09:00 to Tu 28.02.2023 12:00
- Deregistration possible until Fr 17.03.2023 23:59
Details
max. 35 participants
Language: English
Lecturers
Classes (iCal) - next class is marked with N
Tuesday
02.05.
15:00 - 16:30
PC-Seminarraum 5 Oskar-Morgenstern-Platz 1 1.Untergeschoß
Tuesday
09.05.
13:15 - 16:30
PC-Seminarraum 5 Oskar-Morgenstern-Platz 1 1.Untergeschoß
Tuesday
16.05.
13:15 - 16:30
PC-Seminarraum 5 Oskar-Morgenstern-Platz 1 1.Untergeschoß
Tuesday
23.05.
13:15 - 16:30
PC-Seminarraum 5 Oskar-Morgenstern-Platz 1 1.Untergeschoß
Tuesday
06.06.
13:15 - 16:30
PC-Seminarraum 5 Oskar-Morgenstern-Platz 1 1.Untergeschoß
Tuesday
13.06.
13:15 - 16:30
PC-Seminarraum 5 Oskar-Morgenstern-Platz 1 1.Untergeschoß
Tuesday
20.06.
13:15 - 16:30
PC-Seminarraum 5 Oskar-Morgenstern-Platz 1 1.Untergeschoß
Tuesday
27.06.
13:15 - 16:30
PC-Seminarraum 5 Oskar-Morgenstern-Platz 1 1.Untergeschoß
Information
Aims, contents and method of the course
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.
Assessment and permitted materials
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.
Minimum requirements and assessment criteria
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%.
Examination topics
All material covered in class.
Reading list
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
Association in the course directory
Last modified: Mo 08.05.2023 15:27