The following short courses are offered to AIDA Students (PhD students, Post-doc researchers, possibly qualified MSc students of AIDA Members) and other students worldwide.

AIDA Members are university partners of ICT48 Projects: AI4Media, HumanE AI Network, VISION.

Student eligibility: PhD students, Post-doc researchers, MSc students of AIDA Members can participate on preferential favorable terms, according to the rules of each short course. Qualified MSc students from AIDA Members can participate on availability basis.

Registration: Interested students should enter the course webpage to register.

Memory Networks

Description: Neural networks with memory capabilities have been introduced to solve several machine learning problems which require to model sequential data such as time series. The most common models are Recurrent Neural Networks and their variants such as Long-Short Term Memories (LSTM) and Gated Recurrent Units (GRU). More recently, alternative solutions exploiting Transformers or Memory Augmented Neural Networks (MANN) have been suggested to overcome the limitations of RNNs. They have distinguishing characteristics that make them substantially different from the early models. . The former directly compares the elements in the input sequence through self-attention mechanisms. The latter uses an external element-wise addressable memory. Differently from RNNs, in MANNs, state to state transitions are obtained through read/write operations and a set of independent states is maintained. An important consideration of Memory Augmented Networks the number of parameters is not tied to the size of the memory. They have been defined both with episodic and permanent external memories. The course will discuss these new memory networks models and their applications.

Institution: University of Florence

Department: Media Integration and Communication Center (MICC)

Short Course

ECTS: 2

Level: PhD

Semester: Spring Semester

Start Day, Duration: 30/04/21 (3h), 07/05/21 (3h)

Language: English

Participation mode: Webex

Participation terms: Free of charge

Lecturer: Alberto Del Bimbo alberto.delbimbo@unifi.it, Federico Becattini federico.becattini@unifi.it

Link to course:

Computational Game Creativity

Description: This course covers the different creative facets of games (visuals, audio, narrative, game design, level design and gameplay) and algorithms that automate the creation of content in one or more of these facets. Autonomous or semi-autonomous computational creators can alleviate the authorial burden of games during development, can lead to interesting and unexpected gameplay experiences and can provide insights on the nature of human creativity. 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.

10:00 – 12:00, February 24 2021 – Introduction to Computational Creativity
10:00 – 12:00, March 3 2021 – Constructive Level Generation
10:00 – 12:00, March 10 2021 – Advanced Level Generation
10:00 – 12:00, March 17 2021 – Rules & Gameplay
10:00 – 12:00, March 24 2021 – Visuals
10:00 – 12:00, April 14 2021 – Audio
10:00 – 12:00, April 21 2021 – Narrative
10:00 – 12:00, April 28 2021 – Mixed-Initiative & Evaluation

Institution: University of Malta

Short Course

ECTS: 5

Level: Master 2

Semester: Spring

Course start, Duration: Starts on Feb. 24 2021, ends on Apr. 28 2021, 8 weeks, Wednesdays 10:00 – 12:00, CET.

Language: English

Participation mode: Zoom

Participation terms: For registration, please contact Renita Agius <renita.agius@um.edu.mt>

Lecturer: Dr. Antonios Liapis, Institute of Digital Games, University of Malta

Course assistance and workshops: Marvin Zammit, Institute of Digital Games, University of Malta Konstantinos Sfikas, Institute of Digital Games, University of Malta
Matthew Barthet, Institute of Digital Games, University of Malta

Link to course: https://sites.google.com/view/msc-in-digital-games-2020/idg5155-computational-game-creativity



Graph Neural Networks and Neural-Symbolic Computation

Description: This is an introductory course to the theory and applications of Graph Neural Networks (GNN) and to related topics in Neural-Symbolic Computation. The course gives the foundations on neural computation involving patterns represented by graphs in fields ranging from computer vision to bioinformatics. In addition, GNN will be presented for different applications in the case of graph-based domains, where inferential processes are expected to involve also the neighbors of vertexes (e.g. social networks). Finally, the diffusion mechanisms taking place by GNN will be integrated with more general Neural-Symbolic models where the decision mechanisms need to be coherent with external representations of environmental knowledge.

  1.  9:00 – 12:00, February 4 2021 – Neural computation on directed graphs, Diffusion
    on graphs, GNN
  2. 9:00 – 12:00, February 11 2021 – Convolution on graphs, lab experiments
  3. 9:00 – 12:00, February 18 2021 – Neuro-symbolic computation
  4. 9:00 – 11:00, February 25 2021 – Lab experiments
  5. 11:00 – 12:00, February 25 2021 – Seminar by Petar Veličković, Deep Mind

Institution: Université Côte d’Azur

Short Course

ECTS: N/A

Level: Master 2

Semester: Spring

Course start, Duration: Starts on Feb. 4 2021, ends on Mar. 4 2021, 5 weeks, Thursdays 9:00 – 12:00, CET.

Language: English

Participation mode: Zoom

Lecturers: Professor Marco Gori, MAASAI, Université Côté d’Azur and SAILab, University of Siena
Course assistance and seminars:

  • Dr. Petar Veličković, Deep Mind
  • Dr. Michelangelo Diligenti, Google and SAILab, University of Siena
  • Dr. Giuseppe Marra, KU Leuven
  • Matteo Tiezzi, SAILab, University of Siena

For registration, please contact Lucile Sassatelli lucile.sassatelli@univ-cotedazur.fr

Link to course: http://web.univ-cotedazur.fr/en/idex/formations-idex/data-science/

Multimedia Data Analysis and Machine Learning

Description: Data pre-procesing and multimedia content representation. Data clustering, Data classification

Institution: University “Politehnica” of Bucharest, Romania

Department: 

Short Course

ECTS: 

Level: 

Semester: Spring Semester

Start Day, Duration: March 8th 2021

Language: English

Participation mode: 

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Lecturer: Prof. Bogdan Ionescu

Link to course:

Computer Vision and Image Processing

Description:

  • Image Processing: Introduction to Image Processing and Computer Vision, Image Formation, Image Sampling, 2D Systems, Image Transforms, Fast 2D Convolution Algorithms, Image Perception, Image Filtering.  
  • Computer Vision: Edge Detection, Region Segmentation, Texture Description, Shape Description, Image Acquisition, Camera Geometry, Stereo and Multiview Imaging, Structure from Motion, 3D Robot Localization and Mapping, Object Tracking.

Institution: Aristotle University of Thessaloniki

Department: Department of Informatics

Short Course

ECTS: 1.5

Level: MSc/Senior undergraduate

Semester: Spring Semester

Start Day, Duration: 17th March 2021, 2 Days

Language: English

Participation mode: teleconference/tele-exams

Participation terms: See course website

Lecturer: Prof. Ioannis Pitas, pitas@csd.auth.gr

Link to course: https://icarus.csd.auth.gr/cvml-short-course-computer-vision-image-processing/

Explainability and Interpretability in Machine Learning

Description: 

Institution: HES-SO Valais Techno-Pôle, Switzerland

Department: 

Short Course

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Level: 

Semester: Spring Semester

Start Day, Duration: 

Language: English

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Lecturer: Prof. Henning Müller henning.mueller@hevs.ch Prof. Mara Graziani mara.graziani@hevs.ch

Link to course:

Explainability in Machine Learning

Description: 

Institution: University of Bordeaux

Department: 

Short Course

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Level: 

Semester: Spring Semester

Start Day, Duration: 

Language: English

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Lecturer: Prof. Jenny Benois Pineau jenny.benois-pineau@u-bordeaux.fr, Dr. Georges Quenot Georges.Quenot@imag.fr

Link to course:

UCA Deep Learning School 2021

Description: 

Institution: Université Cote d’Azur

Department: 

Short Course

ECTS: 

Level: 

Semester: Summer Semester

Start Day, Duration: June 2021

Language: English

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Lecturer:

Link to course:

Game Artificial Intelligence

Description: 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.

Institution: University of Malta

Department: 

Short Course

ECTS: 

Level: Graduate

Semester: Fall Semester

Start Day, Duration:

Language: English

Participation mode: 

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Lecturer: Prof. Georgios N. Yannakakis georgios.yannakakis@um.edu.mt

Link to course: https://sites.google.com/view/msc-in-digital-games-2020/idg5301-game-ai