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Root number
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484704 |
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Semester
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HS2023 |
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Type of course
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Seminar |
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Allocation to subject
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Sociology |
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Type of exam
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not defined |
| Title |
Machine Learning and Artificial Neural Networks |
| Description |
Durchgeführt von Sebastian Bahr and Martina Jakob
This course provides an introduction to machine learning with a focus on artificial neural networks. It combines a theoretical discussion of different model architectures with hands-on experience in Python using the Keras library. We will cover concepts such as training & testing, optimization, cross-validation, regularization, feature engineering, regression vs. classification, convolutional neural networks, and transfer learning. Based on applications in computer vision and natural language processing (NLP), we will learn how to apply deep learning models to different prediction tasks.
Prerequisites:
Mandatory lectures of the introduction year (Einführungsstudium) need to be successfully completed.
Intermediate Python knowledge (Programming II of Minor “Digitalization and Applied Data Science in Business, Economics and the Social Sciences” or equivalent) is mandatory.
High school level knowledge of linear algebra and derivatives is recommended.
Students need to bring their own device.
Form of Implementation: Presence
Inscription:
From August 15th 2023 (08:00 pm), onwards via ILIAS |
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ILIAS-Link (Learning resource for course)
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No registration/deregistration in CTS (Admission in ILIAS possible).
ILIAS
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Link to another web site
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| Lecturers |
Prof. Dr.
Axel Franzen, Department of Social Sciences ✉
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ECTS
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6 |
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Recognition as optional course possible
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No |
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Grading
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1 to 6 |
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| Dates |
Monday 10:15-12:00 Weekly
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Wednesday 31/1/2024 23:50-23:55
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| Rooms |
Hörraum B 102, Institutsgebäude vonRoll
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| Students please consult the detailed view for complete information on dates, rooms and planned podcasts. |
User:
Öffentlicher Bereich
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