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Module Number:
| 14906
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| Module Title: | Language Models: Machine Learning Basics to Modern Artificial Intelligence |
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Sprachmodelle: von Grundlagen des maschinellen Lernens zur modernen Künstlichen Intelligenz
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| Department: |
Faculty 1 - Mathematics, Computer Science, Physics, Electrical Engineering and Information Technology
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| Responsible Staff Member: | -
Prof. Dr. rer. nat. Zander, Thorsten O.
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| Language of Teaching / Examination: | English |
| Duration: | 1 semester |
| Frequency of Offer: |
Every semester
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| Credits: |
6
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| Learning Outcome: | After successful completion of the module students understand core concepts of supervised and unsupervised machine learning with a focus on NLP; can explain and implement representation-learning methods from word embeddings to Transformers; can critically compare assumptions, objectives, trade-offs, and failure modes; can discuss foundation model families (LLMs, VLMs, VLAs); can design basic experiments, evaluate models, and communicate results clearly. |
| Contents: | - Artificial Intelligence: definitions, scope, history, limitations
- Machine Learning vs. Deep Learning: formulations, data, losses, generalization; MNIST case study
- NLP fundamentals: text as data, tokenization, representations
- Word embeddings: motivation, distributional semantics, strengths and weaknesses
- CNNs and RNNs: explanation, comparison and analysis
- Transformers: self-attention intuition, components, impact
- Language Models and LLMs: pretraining, scaling, evaluation, failure modes
- Practical use of LMs: fine-tuning, prompting, RAG, reasoning behavior and constraints
- Overview of multimodal models: VLMs, VLAs etc.
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| Recommended Prerequisites: | - basic Python programming
- basic linear algebra
- basic probability/statistics
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| Mandatory Prerequisites: | None |
| Forms of Teaching and Proportion: | -
Lecture
/ 2 Hours per Week per Semester
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Seminar
/ 2 Hours per Week per Semester
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Self organised studies
/ 120 Hours
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| Teaching Materials and Literature: | - Bishop, Pattern Recognition and Machine Learning
- Manning & Schütze, Foundations of Statistical Natural Language Processing
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| Module Examination: | Final Module Examination (MAP) |
| Assessment Mode for Module Examination: | - written examination, 90 minutes
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| Evaluation of Module Examination: | Performance Verification – graded |
| Limited Number of Participants: | 25 |
| Part of the Study Programme: | -
Master (research-oriented) /
Artificial Intelligence /
PO 2022
- 1. SÄ 2024
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Master (research-oriented) /
Informatik /
PO 2008
- 3. SÄ 2024
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Master (research-oriented) /
Mathematical Data Science /
PO 2025
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Master (research-oriented) /
Mathematics /
PO 2025
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Bachelor (research-oriented) /
Medizininformatik /
PO 2016
- 1. SÄ 2024
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Master (research-oriented) /
Micro- and Nanoelectronics /
PO 2024
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| Remarks: | - Study programme Artificial Intelligence M.Sc.: Compulsory elective module in complex „Learning and Reasoning”
- Study programme Informatik M.Sc.: Compulsory elective module in complex „Angewandte und Technische Informatik” (Niveaustufe 400)
- Study programme Mathematics M.Sc.: Compulsory elective module in complex „Applications”
- Study programme Mathematical Data Science M.Sc.: Compulsory elective module in complex „Fundamentals of Data Science”
- Study programme Micro- and Nanoelectronics M.Sc.: Compulsory elective module in complex „Applications”
- Study programme Medizininformatik B.Sc.: Compulsory elective module in complex „Informatik”
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| Module Components: | - Lecture: Language Models: Machine Learning Basics to Modern Artificial Intelligence
- Accompanying seminar
- Related examination
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| Components to be offered in the Current Semester: | |
