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, ELISE, HumanE AI Network, TAILOR, 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.

Short Course Offers

Description: Introduction to Machine Learning, Artificial Neural Networks, Perceptron, Multilayer perceptron. Backpropagation, Deep neural networks. Convolutional NNs, Deep learning for object detection, Deep Semantic Image Segmentation, Generative Adversarial Networks, Recurrent Neural Networks. LSTMs, Data Clustering, Decision Surfaces. Support Vector Machines, Distance-based Classification, Dimensionality Reduction, Kernel Methods, Bayesian Learning, Deep Reinforcement Learning, CVML Software Development Tools.

Institution: Aristotle University of Thessaloniki

Department: School of Informatics

Short Course

ECTS: 1.5

Level: MSc/Senior undergraduate

Semester: Spring Semester

Start Day, Duration: 27th April 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/spring-cvml-short-course-machine-learning-and-deep-neural-networks/

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Institution: HES-SO Valais Techno-Pôle, Switzerland

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Short Course

ECTS: 1.5

Level: PhD

Semester: Spring Semester

Start Day, Duration: June 2021

Language: English

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

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Short Course

ECTS: 1.5

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Semester: Fall semester

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Lecturer: Assoc. Prof. Andrew D. Bagdanov

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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: 1.5

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

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Description: 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.

Institution: UNIFI

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ECTS: 1.5

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Lecturer: Prof. P. Frasconi

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Institution: UNIFI

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Institution: Aristotle University of Thessaloniki

Department: School of Informatics

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ECTS: 1.5

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

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Description: Introduction, Data representation, Content description (color, shape, texture, motion, temporal structure, interest points, audio, text), Normalization, Decorrelation. Programming assignments in C/C++ and MATLAB.

Institution: UPB

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ECTS: 1.5

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Description: Introduction, Clustering: data similarity, hierarchical clustering, k-means, Classification: k-NN, Support Vector Machines. Programming assignments in C/C++ and MATLAB.

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Description: several example application domains; the course can include a large part on evaluation and benchmarking of multimedia retrieval (ImageCLEF, MediaEval).

Institution: CNR, HES-SO

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ECTS: 1.5

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Description: 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.

Institution: IDIAP

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Very Short Course

ECTS: N/A

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Semester: Summer/Winter

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Lecturer: D. Gatica-Perez gatica@idiap.ch

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ECTS: 1.5

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Description: 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.

Institution: KUL

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Short Course

ECTS: 1.5

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Semester: Spring

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Lecturer: D. Burk

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Description: Introduction & applications, Image/video representation, Color representation, Point operations, Linear/non-linear filtering. Programming assignments in C/C++ and MATLAB.

Institution: UPB

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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: 1.5

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

Description: 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.

Institution: University of Malta

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ECTS: 1.5

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Start Day, Duration: starting Monday 22/02 and run for a full semester

Language: English

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Lecturer: Prof. Georgios N. Yannakakis

Link to course: https://sites.google.com/view/msc-in-digital-games-2020/idg5159-player-modeling

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

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Short Course

ECTS: 1.5

Level: Graduate

Semester: Fall Semester

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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

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