Module CS4514-KP12

Duration

1 Semester

Turnus of offer

each winter semester

Credit points

12

Course of studies, specific fields and terms:

• Master Robotics and Autonomous Systems 2019, optional subject, Additionally recognized elective module
• Certificate in Artificial Intelligence, compulsory, Artificial Intelligence
• Master Entrepreneurship in Digital Technologies 2020, advanced module, specific
• Master Computer Science 2019, optional subject, Canonical Specialization Data Science and AI
• Master IT-Security 2019, advanced module, Elective Computer Science
• Master Computer Science 2019, optional subject, advanced module

Classes and lectures:

• CS4514-P: Lab course Intelligent Agents (practical course, 2 SWS)
• CS4514-V: Intelligent Agents (lecture with exercises, 6 SWS)

Workload:

• 45 hours exam preparation
• 120 hours in-classroom work
• 195 hours private studies

Contents of teaching:

• Agents, Mechanisms, and Collaboration: Intelligent agents and artificial intelligence / Game theory and social choice / Mechanism design, algorithmic mechanism design / Agent collaboration, rules of encounter / Continuous Space / Epistemic logic / Knowledge and seeing / Knowledge and time / Dynamic epistemic logic / Knowledge-based programs
• Perception (Language and Vision): Information retrieval and web-mining agents / Probabilistic dimension reduction, latent content descriptions, topic models, LDA, LDA-HMM / Representation learning for sequential structures, embedding spaces, word2vec, CBOW, skip-gram, hierarchical softmax, negative sampling / Language models (1d-CNNs. RNNs, LSTMs, ELMo, Transformers, BERT, GPT-3/OPT, and beyond), Natural language inference and query answering / Computer Vision (2D-CNNs, Deep Architectures: AlexNet, ResNet) /Combining language and vision (CLIP (OpenAI) / LIT (Google) / data2vec (Facebook) / Flamingo (DeepMind), DALL-E and beyond) /Knowledge graph embedding with GNNs, combining embedding-based KG completion with probabilistic graphical models (ExpressGNN, pLogicNet), MLN inference and learning based on embedded knowledge graphs, GMNNs)
• Planning, Causality, and Reinforcement Learning: Planning and acting with deterministic models, temporal models, nondeterministic models, probabilistic models / Standard decision making / Advanced decision making and reinforcement learning / Causal dependencies / Intervention / Instrumental variables / Counterfactuals / Causal planning / Causal reinforcement learning
• In the project lab students use the usual (open source) data science related programming languages and tools in order to transfer the abstractions, concepts and results taught in the lecture into concrete software models and artefacts to be applied on big data.

Qualification-goals/Competencies:

• The students can enumerate central ideas, define the relevant concepts and explain the functioning of algorithms with help of application scenarios for all the items listed in contents of teaching.

Grading through:

• Oral examination

Responsible for this module:

• Prof. Dr.-Ing. Nele Rußwinkel

Teacher:

• Institute of Information Systems
• Prof. Dr.-Ing. Nele Rußwinkel

Literature:

• J. Pearl, C. Glymour, and N.P. Jewell : Causal Inference in Statistics - A Primer Wiley, 2016
• Y. Shoham, K. Leyton-Brown : Multiagent-Systems: Algorithmic, Game-Theoretic, and Logical Foundations Cambridge University Press, 2009
• S.J. Russell, P. Norvig : Artificial Intelligence: A Modern Approach Pearson, 2020
• M. Ghallab, D. Nau, P. Traverso : Automated Planning and Acting Cambridge University Press, 2016

Language:

• offered only in English

Notes:

Last Updated:

06.01.2025