4- Full Course: Advanced Artificial Intelligence Technologies and Applications
Dalian University of Technology, China, Mar - Jun 2023
Course organizer: A/Prof. Shihua Zhou
Course presenter: Prof Nikola Kasabov; Assistants: A/Prof Wei Qi Yan and Ms. Iman AbouHassan.
References: N.Kasabov, Time-Space, Spiking Neural Networks and Brain-Inspired Artificial Intelligence Springer, 2019.
Course Content
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AI and the evolution of its principles. Evolving processes in Time and Space (Ch.1, p.3-19) (Lecture1)
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From Data and Information to Knowledge. Fuzzy logic. (Ch.1, p.19-33 + extra reading) (Lecture2)
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Artificial neural networks - fundamentals. (Ch.2, p.39-48 + extra reading) (Lecture3)
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Deep neural networks (Ch.2, p.48-50 + extra reading)
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Evolving connectionist systems (ECOS) (Ch.2, p.50-78). NeuCom software (IA)
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Deep learning and deep knowledge representation in the human brain (Ch.3)
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Spiking neural networks (Ch.4). Evolving spiking neural networks (Ch.5)
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Brain-inspired SNN. NeuCube. (Ch.6). NeuCube software (IA)
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Evolutionary and quantum-inspired computation (Ch.7)
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AI applications in health (Ch.8-11)
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AI applications for computer vision (Ch.12,13)
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AI for brain-computer interfaces (BCI) (Ch.14)
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AI for language modeling. ChatBots (extra reading)
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AI in bioinformatics and neuroinformatics (Ch.15,16,17,18)
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AI applications for multisensory environmental data (Ch.19)
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AI in finance and economics (Ch.19)
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Neuromorphic hardware and neurocomputers (Ch.20).
3- Short Course: Neural Networks, Deep Learning, and Brain-Inspired Artificial intelligence
Dalian University of Technology, China, Aug 2022
Course organizer: A/Prof. Shihua Zhou
Course presenter: Prof Nikola Kasabov; Assistants: A/Prof Wei Qi Yan and Ms. Iman AbouHassan.
References: N.Kasabov, Time-Space, Spiking Neural Networks and Brain-Inspired Artificial Intelligence Springer, 2019.
Wei Qi Yan, Computational methods for deep learning, Springer, 2022.
Course Content
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Artificial neural networks - fundamentals. Evolving connectionist systems (ECOS) (NK, Chapters 1,2)
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NeuCom software. Incremental and transfer learning in ECOS (IA, NK).
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Deep neural networks (DNN) (WQY, NK)
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Deep learning algorithms (WQY, NK)
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DNN for image and video (WQY, NK)
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Brain information processing (Chapter 3).
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Spiking neural networks and evolving SNN (NK, Chapters 4,5)
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Brain-inspired computational architectures. NeuCube software (NK, IA, Chapter 6).
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Evolutionary and quantum computation (NK, Chapter 7).
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Applications of NN and SNN for financial and economic data modeling (IA, NK, Chapter 19).
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Other applications of NN, DNN, and SNN. Overview of the course (NK, WQY, IA)
2- Short Course: Deep Learning in Neural Networks and Applications
The Dalian University of Technology, China, Jun 2022
Course organizer: A/Prof. Shihua Zhou
Lecturers: Prof Nikola Kasabov; A/Prof Wei Qi Yan; Ms. Iman AbouHassan
References: N.Kasabov, Time-Space, Spiking Neural Networks and Brain-Inspired Artificial Intelligence Springer, 2019.
Wei Qi Yan, Computational methods for deep learning, Springer, 2022.
Course Content
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Introduction to Neural Networks and Deep Neural Networks (DNN).
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Algorithms for Deep Learning 1.
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Algorithms for Deep Learning 2.
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Algorithms for Deep Learning 3.
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Reinforcement learning.
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Incremental Learning (IL) and Transfer Learning (TL).
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IL and TL of vector-based data in evolving neuro-fuzzy systems, exemplified by EFuNN & DENFIS in NeuCom.
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IL and TL in brain-inspired SNN, exemplified by NeuCube.
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1- Short Course: Advanced Neural Networks for AI: Methods, Systems, Applications
The Dalian University of Technology, China, May 2022
Course organizer: A/Prof. Shihua Zhou
Lecturers: Prof Nikola Kasabov; Ms. Iman AbouHassan
Reference: N.Kasabov, Time-Space, Spiking Neural Networks and Brain-Inspired Artificial Intelligence Springer, 2019.
Course Content
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Artificial Neural Networks Fundamentals: NeuCom software.
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Spiking Neural Networks.
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Brain-Inspired Computational Architectures: NeuCube software.
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Evolutionary and Quantum Computation.
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Overview of applications of SNN for brain and genetic data modeling.
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Applications of SNN for streaming multisensory predictive data modeling.