(Under Construction)

AI Core Subjects (AI4Media Input only so far)

Machine Learning (3IA-UCA)

Syllabus: To be completed.

Offered Courses:

  • Foundations of Pattern recognitionStatistical machine learning.
    Credits and details: ECTS 5, Senior undergraduate course, Spring semester, Greek/English, Participation: teleconference/tele-exams/project Lecturer: I. Pitas, pitas@csd.auth.gr
    Syllabus: Random variables and vectors. Decision functions. Classification algorithms utilizing decision functions. Classification based on distance. Classification based on decision theory. Principal component analysis. Linear discriminant analysis. Estimation of probability distribution parameters. Analysis of similarity and web graphs. Syntactic pattern recognition. Vector quantization techniques. Programming assignments in C/C++ and MATLAB. 
    Link to Course: https://qa.auth.gr/en/class/1/600121270/M1
  • Machine Learning for Visual Data Analysis (QMUL)
    Credits and details: Short Course, Graduate
    Lecturer: TBD
    Syllabus: TBD
  • Machine Learning for functional, mixed and text data 3IA – UCA
    Credits and details: ECTS 4, Master 2, Fall semester, English
    Lecturers: C. Bouveyron, Frederic Precioso, Marco Winckler
    Syllabus: visual mining, time series clustering and embedding, statistical models on graph data, embedding of heterogeneous (structured and unstructured data)
    Link to Course: http://web.univ-cotedazur.fr//en/idex/formations-idex/data-science/
  • Statistical Learning in High Dimensions 3IA-UCA
    Credits and details: ECTS:4, Master 2, Fall semester, English
    Lecturers: C. Bouveyron, P.-A. Mattei
    Syllabus: statistical analysis of high-dimensional data for classification, clustering, regression, and collaborative filtering, study of sparse methods (lasso, elastic net, sparse Bayesian methods), probabilistic PCA (and mixtures thereof), nuclear norm penalisation. Labs in R and Python.
  • Explainability and Interpretability in machine learning  TBD
    Credits and details: Short course, Graduate
    Lecturer: TBD
    Syllabus: TBD

Reinforcement Learning (TBD)

  • Introduction to Reinforcement Learning UNIFI (TBD)
    Credits and details: Short Course, Graduate
    Lecturer: Andrew D. Bagdanov (TBD)
    Syllabus: (TBD)

Deep Learning (UNIFI)

  • Introduction to Deep Learning UNITN
    Credits and details: ECTS 6, Full course, Graduate course, Spring semester, English
    Lecturers: Elisa Ricci (e.ricci@unitn.it)
    Syllabus: The course aims to provide students with an overview of the main models and applications of deep learning. In particular, the first part of the course will introduce the basic concepts related to deep learning and to the training of artificial neural networks (Backpropagation, Dropout, BatchNorm, …). In the second part, the main types of neural models will be presented. Convolutional Neural Networks, Recurrent Neural Networks, Generative Adversarial Networks, Deep Reinforcement Learning will be introduced. In the final part of the course some applications of deep learning will be presented in the field of computer vision, robotics and natural language processing. Theoretical discussion will be complemented with lab in Python using open-source deep learning libraries.
  • Deep Learning 2 3IA-UCA
    Credits and details: ECTS 4, Master 2, Fall semester, English
    Lecturers: M. Riveill, D. Lingrand
    Syllabus: CNN, RNN, attention models for RNN, hyper-parameter tuning, convolutional and variational auto-encoders, Applications to (1) NLP: Word embedding, Sentiment analysis, Naming Entity Recognition, Part of speech tagging, (2) image processing: feature and image extraction, image denoising.
  • Memory Networks UNIFI
    Credits and details: Short Course, Graduate
    Lecturer: Federico Becattini, Alberto del Bimbo
    Syllabus: Memory Networks, Recorrent networks, LSTM, Stacked and bidirectional LSTM, Memory Augmented Neural Networks, Neural turing Machines, hands on, Applications
  • Graph Neural Networks 3IA-UCA
    Credits and details: ECTS 4, Master 2, Fall semester, English
    Lecturer: M. Gori
    Syllabus: (TBD)
  • Adversarial Learning and Explainable AI  UNIFI
    Credits and details: ECTS 3, Short Course, Graduate, English
    Lecturer: P. Frasconi
    Syllabus: The first will be structured in two subsections. In the first part the course will revise some evasive and poisoning attacks to learning systems and ideas for defending against them. In the second part the curse will address some approaches to interpretability in machine learning.
  • Advanced Deep Learning 3IA-UCA
    Credits and details: ECTS 4, Master 2, Fall semester, English
    Lecturer: F. Precioso, P. -A. Mattei
    Syllabus: Deep neural architectures for multi-modal media data, Deep generative models, Generative Adversarial Networks, Bias mitigation, Deep networks interpretation and explanation, latest research challenges in DL (double gradient descent, exponential learning rate, etc.)

AI and Media Courses

Visual Media (AUTH)

  • Computer Vision 
    Credits and details: ECTS 5, Full course, Senior undergraduate course, Spring semester, Greek/English, Participation: teleconference/tele-exams/project
    Lecturer: I. Pitas, pitas@csd.auth.gr
    Syllabus: Image segmentation. Image texture. Image features. Image registration. Image search and retrieval. Image topology. 2D shape description and recognition. Moving images. Motion estimation. Object tracking. Video description. Video search and retrieval.
    Link to Course: https://qa.auth.gr/en/class/1/600180123
  • Advanced Computer Vision
    Credits and details: ECTS 7.5, Full course,  Graduate course, Fall semester, Greek/English, Participation: teleconference/tele-exams/project
    Lecturer: I. Pitas, pitas@csd.auth.gr
    Syllabus: Image acquisition. Mathematical modeling of image formation. Introduction to image processing and analysis. Camera calibration. Stereo vision. Depth estimation. Object localization. 3D image analysis. Surface geometry. Object topology. Object landmarks and features. Object recognition. Object registration. Object description. Applications in medical imaging, image retrieval, robotic vision.
    Link to course: https://qa.auth.gr/en/class/1/600177048
  • Deep Learning for Computer Vision QMUL
    Credits and details: Short Course, Graduate
    Lecturer: TBD
    Syllabus: TBD
  • Computer Vision and Deep Learning in Practice UNIFI
    Credits and details: ECTS 3, Short Course, Graduate
    Lecturers: M. Bertini, L. Seidenari, T. Uricchio
    Syllabus: TBD
  • Computer Vision Systems UNITN
    Credits and details: ECTS 6, Full course, Graduate course, Fall semester, English
    Lecturers: Nicu Sebe & Elisa Ricci (niculae.sebe@unitn.it, e.ricci@unitn.it)
    Syllabus: This course will prepare the students in both the theoretical foundations of computer vision as well as the practical approaches to building real Computer Vision systems. This course investigates current research topics in computer vision with an emphasis on recognition tasks and deep learning. We will examine data sources, features, and learning algorithms useful for understanding and manipulating visual data. The goal of this course is to give students the background and skills necessary to perform research in computer vision and its application domains such as robotics. Students should understand the strengths and weaknesses of current approaches to research problems and identify interesting open questions and future research directions. Topics covered will include object detection and segmentation with deep networks, deep generative models for image generation, image captioning, activity recognition, video generation, deep transfer learning and few-shot learning.
  • Computer vision and Machine Learning for Autonomous Systems AUTH
    Credits and details: ECTS 2, Short course, Graduate course, Spring semester, English, Participation: local/exams/project
    Lecturer: I. Pitas, pitas@csd.auth.gr
    Syllabus: TBD

Music and Sound (IDMT)

  •  Machine Listening for Music and Sound Recognition  IDMT
    Credits and details: No ECTS, 4 lectures + 3 seminars, graduate student course, Winter semester, English, Participation: teleconference
    Lecturer: J. Abeßer, jakob.abesser@idmt.fraunhofer.dehttps://machinelistening.github.io
    Syllabus: Lecture1: Introduction to Audio Representations. Lecture2: Introduction to Machine Learning. Lecture3: Music Information Retrieval. Lecture4: Machine Listening for Environmental Sounds

Natural Language (TBD)

  •  Natural Language Processing  TBD
    Credits and details: Short Course, Graduate
    Lecturer: TBD
    Syllabus: TBD
  • Speech Recognition TBD
    Credits and details: Short Course, Graduate
    Lecturer: TBD
    Syllabus: TBD

Multimedia (TBD)

  • Multimedia Content Representation  UPB
    Credits and details: hort Course, Graduate, 6-8 hours
    Lecturer: TBD
    Syllabus: Introduction, Data representation, Content description (color, shape, texture, motion, temporal structure, interest points, audio, text), Normalization, Decorrelation. Programming assignments in C/C++ and MATLAB.
  •  Multimedia Classification UPB
    Credits and details: Short Course, Graduate, 6-8 hours
    LecturerTBD
    Syllabus: Introduction, Clustering: data similarity, hierarchical clustering, k-means, Classification: k-NN, Support Vector Machines. Programming assignments in C/C++ and MATLAB.
  • Multimedia information retrieval  CNR, HES-SO
    Credits and details: Short Course, Graduate
    Lecturer:  TBD
    Syllabus: Several example application domains;the course can include a large part on evaluation and benchmarking of multimedia retrieval (ImageCLEF, MediaEval

Web and Social Media (3IA)

  • Web of Data 3IA-UCA
    Credits and details: ECTS:4, Master 2, Fall semester, English
    Lecturer: C. Faron
    Syllabus: Principles of a Web of Linked Data, the RDF Data Model to publish and link data on the Web, the SPARQL query language, Integration of other data formats and sources.
  • Statistical Analysis of Graphs 3IA-UCA
    Credits and details: ECTS:4, Master 2, Fall semester, English
    Lecturer: K. Avrachenkov
    Syllabus: Network inference: estimation of network characteristics (diameter, edge conductances, etc.), testing hypotheses regarding graph structure, rumour source detection, network tomography; Network algorithms: distributed learning and optimization, clustering.
  • Human-Centered Social Media (IDIAP)
    Credits and details: No ECTS credits, Short Course, Graduate (can be offered as part of a summer or winter school) t in English.
    Lecturer: D. Gatica-Perez, gatica@idiap.ch
    Syllabus: The course presents a human-centered, multidisciplinary view of social  media. It integrates concepts from media studies, multimedia information systems, and machine learning to understand user motivations and behavior, and analyze content of socio-technical systems like Twitter, Facebook, and YouTube. Students will become familiar with approaches for classification, discovery, and interpretation of social media phenomena.

AI and Society Courses

Human-computer interaction (UNITN)

  • Affective Computing UNITN
    Credits and details: ECTS 6, Full course, Graduate course, Fall semester, English
    Lecturer: N. Sebe, niculae.sebe@unitn.it
    Syllabus: This course explores computing research that relates to, arises from, or deliberately influences emotion. The aim is to identify the important research issues, and to ascertain potentially fruitful future research directions in relation to the multimodal emotion analysis and to human-computer interaction. The course will introduce key concepts, discuss technical approaches, and open issues in the following areas: interaction of emotion with cognition and perception; the role of emotion in human-computer interaction; the communication of human emotion via face, voice, physiology, and behavior; construction of computers that have skills of emotional intelligence; the development of computers that “have” emotion; and other areas of current research interest in the research community.
  • Human-robot interactivity TBD
    Credits and details: Short Course, Graduates
    Lecturer:  TBD
    Syllabus: TBD

AI Ethics (KUL)

  • AI Ethics and Regulation (KUL)
    Credits and details: ECTS 4, Master 2, Spring semester, English
    Lecturer: P. Valcke & A. Vedder
    Syllabus: online materials covering following topics: *Requirements of trustworthy AI: Ethical and legal perspectives, critical discussion and implementation (autonomy and personhood; safety and security; justice; enforcement and regulatory oversight); *Dual Use: ethical backdrop, policies and implementation; *Comparison EU with non-EU perspectives on ethical / trustworthy AI; *Case studies (including media & fake news).
  • IP/IT Law and Emerging Technologies (KUL)
    Credits and details: ECTS: 3, Short Course, Graduate, Master 2, Spring semester, English
    Lecturer: D. Burk
    Syllabus: online materials covering following topics: *emerging legal issues in the fields of intellectual property rights, liability, privacy and data protection in relation to new technologies, in particular Artificial Intelligence, Nanotechnology, Neurotechnology, Biotechnology and Robotics.

AI4Media Elective Topics

Image Processing and Analysis (AUTH)

  • Image Processing AUTH
    Credits and details: ECTS:5, Full course, Senior undergraduate course, Spring semester, Greek/English, Participation: teleconference/tele-exams/project
    Lecturer: I. Pitas, pitas@csd.auth.gr
    Syllabus: Digital image recording/digitization. Image enhancement and filtering. Image restoration. Digital image compression and related standards (e.g. JPEG/JPEG2000). Edge detection algorithms. Image segmentation. Shape description. Programming assignments in C/C++ and MATLAB.
    Link to course: https://qa.auth.gr/en/class/1/600176703
  • Pre-processing of Visual Information UPB
    Credits and details: Short Course, Graduate, 6-8 hours
    Lecturer: TBD
    Syllabus: Introduction & applications, Image/video representation, Color representation, Point operations, Linear/non-linear filtering. Programming assignments in C/C++ and MATLAB

Games (UM)

  • Computational Game Creativity UM
    Credits and details: Short Course, Graduate TBD
    Lecturer: TBD
    Syllabus: Introduction, Level & World Generation, Rules and Mechanics, Experience-driven Procedural Content Generation (PCG), Visuals & Audio, Narrative, Mixed-Initiative PCG, Evaluating PCG The study-unit includes a study of the different game facets from the perspective of human authors, presents the state-of-the art in procedural content generation within games, and connects them to instances of computational or mixed-initiative creativity outside of games (e.g. parametric design in architecture, generative art, procedural audio). Computational game creativity is positioned at the intersection of developing fields within games research, such as procedural content generation and AI-assisted design, and long-studied fields, such as visual art and narrative.
  •  Player Modeling: From Game Analytics to Affective Computing UM
    Credits and details: Short Course, Graduate TBD
    Lecturer: TBD
    Syllabus: Revisiting game artificial intelligence. The role of Player Modeling, Basic data analysis, data preprocessing and descriptive statistics, Classification and prediction, Clustering, Data Visualization, Industrial game analytics – problems and needs, Theories of emotion (affect and cognition), The Affective Loop: key components, Eliciting Emotion (protocols and approaches), Recognizing and Modelling Emotion, The model’s input (Speech, eye gaze, physiology, images, movement/posture), Feature Extraction / Selection, The model’s output (affect annotation / ranks, ratings, ground truth), A taxonomy of modelling approaches, Pattern recognition, Classification, Regression, Preference Learning, Expressing Emotion (via agents and virtual environments), Closing the affective loop: Adaptation via agents and virtual environments, Player Experience Modeling, Popular application domains: computer games, HCI, health etc.
  • Game Artificial Intelligence UM
    Credits and details: Short Course, Graduate TBD
    Lecturer: TBD
    Syllabus: Introduction to Game AI, Representation and Utility, Play games, Tree Search and Monte Carlo Tree Search, Play via Supervision, Play via Reinforcements, Play via Evolutionary and Genetic Algorithms, Play for Winning, Diversity and Testing. The study-unit aims to introduce students to the theory of basic and advanced game artificial intelligence topics and provide hands-on experience on the implementation of popular algorithms on commercial-standard games. Course page: https://sites.google.com/view/msc-in-digital-games-2020/idg5301-game-ai

Media production (AUTH)

  •  Autonomous Systems for media production AUTH
    Credits and details: ECTS 5, Full course, Graduate course, Spring semester, Greek/English, Participation: teleconference/tele-exams/project
    Lecturer: I. Pitas, pitas@csd.auth.gr
    Syllabus: (TBD )
    Link to course: https://qa.auth.gr/en/class/1/600176703