Lehrgebiet

Intelligente Systeme WS 2016/17

Andreas Dengel

Fragestellungen

Automatisierung von „intelligentem" Verhalten im Sinn eines Leistungsverstärkers für den Menschen, z.B. •  Verstehen von Bildern, Sprache und Texte •  Ziehen von Schlüssen im Sinne von

Entscheidungsunterstützung, Simulation, Vorhersage •  Software; die eigene Schlüsse zieht, die begründet, plant

und Interpretation von Sensordaten und Nutzerverhalten •  Kontext-sensitive Unterstützung

ARBEITSGRUPPEN

AG Augmented Vision

Physical World

Virtual World

Internet of Things

Internet of Services

AG Eingebettete Intelligenz

Physical World

Virtual World

Internet of Things

Internet of Services

AG Wissensbasierte Systeme

Physical World

Internet of Things

Internet of Services

Virtual World

KERNVORLESUNGEN

Einführung in die Symbolische Künstliche Intelligenz http://agd.cs.uni-kl.de/teaching

Lecture: 2C+1R, 4 CP, Deutsch, oral exams, each WS •  Daten, Information, Wissen •  Prinzipien Wissensrepräsentation und - verarbeitung •  Fuzzy Logic •  Suchverfahren •  Zweipersonenspiele •  Constraint Satisfaction

Seminar: 2S, 4 CP, Deutsch, each SS •  Ausgewählte Themen aus dem Bereich KI •  Theoretical work •  Studien, Analyse, Publikation & Vortrag im Konferenzstil

Project: 4P, 8 CP, English, each SS •  Ausgewählte Themen aus dem Bereich KI •  Practical work / implementation

89-7002 Einführung in die Statistische KI

Die Vorlesung behandelt subsymbolische Methoden, einschließlich statistischer und neuronaler Methoden. • maschinelles Lernen • neuronale Netzwerke • Neuroinformatik • Wahrnehmung und Steuerung • Bildverarbeitung, Multimediale Datenbanken • Data Mining • Informationssicherheit • Bayessche Methoden

SCHWERPUNKTVORLESUNGEN

Collaborative Intelligence http://agd.cs.uni-kl.de/teaching

Lecture: 2C+1R, 4 CP, English, oral exams, each WS •  Search and Classification •  Recommender Systems •  Attention Recognition •  Agile Semantic Technologies •  Document Analysis & Information Extraction •  Interaction and Visualization •  Social Media Monitoring and Analysis

Seminar: 2S, 4 CP, English, each SS •  Selected topics from the field of

Collaborative Intelligence •  Theoretical work

Project: 4P, 8 CP, English, each SS •  Selected topics from the field of

Collaborative Intelligence •  Practical work / implementation

Applications of Artificial Intelligence apl.-Prof. Dr. Marcus Eichenberger-Liwicki

What you will learn: Categorize a given real-world problem and know which AI methods are appropriate Standard concepts for defining the problem situation Get a broad overview over AI methods Know existing tools which implement AI methods Exercises with different toolkits Know how to avoid pitfalls Topics covered: 1.  Automated Speech Recognition 2.  Handwriting Recognition 3.  Embodied Computing 4.  Robotics

Social Web Mining Dr. Darko Obradovic / Prof. A. Dengel

What you will learn: Acquisition of knowledge about methods and technologies for the data-driven analysis of Social Web content. This includes especially online social networking sites, interactive web platforms and e-mails. This lecture teaches methods for online data collection, and guidelines for the selection and application of Data Science methods that are particularly well suited for Social Web content. Topics covered: •  RESTful APIs •  RSS and Atom Syndication •  Web Crawling and Web Scraping •  Data Mining •  Text Mining •  Network Mining

Multimedia Data Mining Dr. Damian Borth / Prof. A. Dengel

What you will learn: This course provides a comprehensive understanding of multimedia content, retrieval, and mining by introducing students to the formats, techniques, and algorithms used to represent, store, process, analyze, retrieve multimedia content. Topics covered include.

Topics covered: •  models of information retrieval •  performance evaluation and competitions •  query based retrieval, nearest neighbors, hash algorithms •  topic models and vector space models •  text categorization, analysis, tagging, parsing •  content based image and video retrieval •  image retrieval based on color, texture, and shape •  visual bag of words model •  automatic annotation and categorization •  generative and discriminative methods •  deep learning by convolutional neural networks •  applications in consumer imaging, forensics, and copyright

detection, visual sentiment analysis, emotion detection

Semantische Technologien Sven Hertling / Prof. A. Dengel

What you will learn: You will be able to model knowledge in the Semantic Web and provide it in the Linked Open Data Cloud. The Semantic Web (Web 3.0, Linked Data Web) enables data to be interlinked from any source and to be understood by computers. This means data can be interpretated, aggregated and evaluated by machines to help people finding the right content.

Topics covered: • Understanding of technical foundations (HTTP,REST,Microdata,XML) • Learning about RDF (Resource Description Framework) for exchanging data • Knowledge representation (schema) with semantic technologies • Semantic Web search using SPARQL • Learning to program the Semantic Web • Publishing data in LOD (Linked Open Data Cloud) • Modeling a shared understanding with OWL • Reasoning within the Semantic Web • Modelling of ontologies/schemas (Ontologie Engineering) • Creating rules with SWRL (Semantic Web Rule Language) • Information Extraction • Ontology Learning • Semantic Desktop and further applications

Document and Content Analysis Dr. Saqib Bukhari / Prof. A. Dengel

What you will learn: Most of the data we interact with day-to-day does not come in the form of data structures or databases, but instead in the form of documents and document images. This course introduces students to the formats, techniques, and algorithms used for representing, compressing, analyzing, processing, and displaying documents.

Topics covered: •  document formats and standards (TIFF, JPEG, PDF, PostScript, SVG) •  document image compression (G4, MRC, token based compression, JPEG2000) •  logical markup (HTML, XML, word processing formats, DocBook) •  writings systems of the world •  character sets and character encodings (ASCII, Unicode, special coding systems) •  text rendering, layout, ligatures, and hyphenation (Pango) •  typesetting and page layout systems (text flow, Word, LaTeX, etc.) •  OCR (character recognition, page segmentation) •  spelling and orthographic variation, statistical language modeling •  document capture, page image dewarping and handheld document capture) •  named entity recognition, information extraction, table recognition •  document search and retrieval, text mining, document databases •  reading, psychophysics, and human-document interaction •  document security and forensics

Very Deep Learning - Recent Methods and Technologies apl.-Prof. Dr. Marcus Eichenberger-Liwicki

What you will learn: In this lecture the most recent advances of deep learning will be presented. The intended schedule is: •  Introduction, Motivation •  Advanced Convolutional Networks (ConvNet, AlexNet, GoogLeNet) •  SqueezeNet •  Extended Recurrent Neural Networks (LSTM, MD-LSTM, Dynamic Cortex Memories) •  Spiking Neural Networks •  Reinforcement Learning (Policy and Value Networks) •  Bleeding-Edge Architectures (depending on most recent publications in Deep Learning). Prerequisites: •  Knowledge of Neural Networks, MLP, Backpropagation •  Recurrent Neural Networks •  Strong knowledge in Linear Algebra and theoretical computer science •  Python programming skills Expected outcomes: •  Understanding and Implementing advanced deep learning methods •  Solving difficult tasks in Pattern Recognition, Data Science, and Big Data Analytics

3D Computer Vision http://ags.cs.uni-kl.de/teaching/

Lecture: 2C+1R, 4 CP, English, oral exams, each WS •  Camera model and camera calibration

• Fitting and parameter estimation • 2D-image transformation (mapping) and panoramas • Two cameras: epipolar geometry and triangulation • Multiple view reconstruction • Depth maps and multiple view stereo reconstruction • Dense tracking and mapping • Structured light: laser, coded light

Seminar: 2S, 4 CP, English, each SS • Selected topics from the field of 3D Computer Vision and Augmented Reality • Theoretical work

Project: 4P, 8 CP, English, each SS • Selected topics from the field of 3D Computer Vision and Augmented Reality • Practical work / implementation

Augmented Reality

2D image processing http://ags.cs.uni-kl.de/teaching/

Lecture: 2C+1R, 4 CP, English, oral exams, each SS • Basics Sensors and color spaces Filters (noise, ..etc..) Image interpolation • Image processing

Edge, corner, blob detection Feature descriptors Feature tracking and optical flow Segmentation

• Advanced systems Object detection / tracking Templates, classification, background subtraction

Seminar: 2S, 4 CP, English, each WS • Selected topics from the field of computer vision and tracking • Theoretical work

Project: 4P, 8 CP, English, each WS • Selected topics from the field of computer vision and tracking • Practical work / implementation

Human motion modelling and capturing http://agw.cs.uni-kl.de/en/teaching-jobs/

Seminar: 2S, 4 CP, English, Master, each semester •  Selected topics related to the simulation, capturing

and analysis of human motion and its applications (e.g., rehabilitation, sport, ergonomy)

•  Topics may come from the areas of, e.g., computer graphics, physics, biomechanics, computer vision, sensor fusion, optimal control and machine learning

•  Theoretical work

Project: 4P, 8 CP, English, each semester •  Research, design, implementation and evaluation of

algorithms and methods for simulating, capturing and analyzing human motion

•  Practical work

Lukowicz

Planung Lehrveranstaltungen W

S 16/17 SS 17

+ VL „Very Deep Learning - Recent Methods and Technologies“, 2V+1Ü, Liwicki

Weitere Informationen:

• Lehrgebiet

o http://agd.cs.uni-kl.de o 

• Arbeitsgruppen o AG Dengel http://agd.cs.uni-kl.de o AG Lukowicz http://www.dfki.de/web/forschung/ei/ o AG Stricker http://ags.cs.uni-kl.de o AG Bleser: www.wearhealth.org