| Artificial Intelligence for Programming [2022/23 WiSe] | ||
|---|---|---|
| Code IAIP |
Name Artificial Intelligence for Programming |
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| LP 6 |
Dauer one semester |
Angebotsturnus at least every 4th semester |
| Format Lecture 2 SWS + Exercise course 2 SWS |
Arbeitsaufwand 180 h; thereof 60 h lecture 15 h preparation for exam 105 h self-study and working on assignments (optionally in groups) |
Verwendbarkeit M.Sc. Angewandte Informatik M.Sc. Data and Computer Science M.Sc. Scientific Computing |
| Sprache English |
Lehrende Artur Andrzejak |
Prüfungsschema |
| Lernziele | Expected learning outcomes are: - Knowledge of selected classical methods in arti-ficial intelligence, in particular knowledge repre-sentation, search methods, rule systems - Basic knowledge about probabilistic models and probabilistic programming - Knowledge of techniques for code representation and parsing - Knowledge of techniques for modeling of code via neural networks - Knowledge of basic and advanced methods for program synthesis - Familiarity with semantic parsing and code sum-marization - Familiarity with selected applications of AI for programming, e.g. code-to-code translation, code recommendations, and detection of bugs in code. |
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| Lerninhalte | This module covers the following topics: - introduction to classical methods in artificial intelligence, in particular knowledge representation, search methods, rule systems - introduction to probabilistic models and probabilistic programming - fundamentals of code representation and parsing - modeling of code via neural networks and sequence models/transformers - basic and advanced methods for program synthesis - introduction to semantic parsing and code summarization - state-of-the-art applications of AI for programming, e.g. code-to-code translation, code recommendations, detection of vulnerabilities in code. |
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| Teilnahme- voraus- setzungen |
Skills in programming (preferably Python) and elementary knowledge of probability theory / statistics. Recommended prerequisites are lectures in machine learning, e.g. Foundations of machine learning. | |
| Vergabe der LP und Modulendnote | The module is completed with a graded oral or written examination. The final grade of the module is determined by the grade of the examination. The requirements for the assignment of credits follows the regulations in section modalities for examinations. | |
| Nützliche Literatur | Stuart J. Russell: Artificial intelligence: a modern ap-proach, (3rd ed.), Pearson, 2016, Heidi: https://bit.ly/2V9LQT9 Noah D. Goodman, Joshua B. Tenenbaum: Probabil-istic Models of Cognition (2nd ed.), 2016. Online: https://probmods.org/ Jeremy Howard: Deep learning for coders with fastai and PyTorch, (1st ed.), O'Reilly, 2020, Online via Heidi: https://bit.ly/3jUMkH7 Aurélien Géron: Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow, (2nd ed.), O'Reilly, 2019, Online via Heidi: https://bit.ly/3dVhieA |
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