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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

  1. AI and the evolution of its principles. Evolving processes in Time and Space (Ch.1, p.3-19) (Lecture1)

  2. From Data and Information to Knowledge. Fuzzy logic. (Ch.1, p.19-33 + extra reading) (Lecture2)

  3. Artificial neural networks - fundamentals. (Ch.2, p.39-48 + extra reading​) (Lecture3)

  4. Deep neural networks (Ch.2, p.48-50 + extra reading)

  5. Evolving connectionist systems (ECOS) (Ch.2, p.50-78). NeuCom software (IA)

  6. Deep learning and deep knowledge representation in the human brain (Ch.3)

  7. Spiking neural networks (Ch.4). Evolving spiking neural networks (Ch.5)

  8. Brain-inspired SNN. NeuCube. (Ch.6). NeuCube software (IA)

  9. Evolutionary and quantum-inspired computation (Ch.7)

  10. AI applications in health (Ch.8-11)

  11. AI applications for computer vision (Ch.12,13)

  12. AI for brain-computer interfaces (BCI) (Ch.14)

  13. AI for language modeling. ChatBots (extra reading)

  14. AI in bioinformatics and neuroinformatics (Ch.15,16,17,18)

  15. AI applications for multisensory environmental data (Ch.19)

  16. AI in finance and economics (Ch.19)

  17. Neuromorphic hardware and neurocomputers (Ch.20).

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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

  1. Artificial neural networks - fundamentals. Evolving connectionist systems (ECOS) (NK, Chapters 1,2)

  2. NeuCom software. Incremental and transfer learning in ECOS (IA, NK).

  3. Deep neural networks (DNN) (WQY, NK)

  4. Deep learning algorithms (WQY, NK)

  5. DNN for image and video (WQY, NK)

  6. Brain information processing (Chapter 3).

  7. Spiking neural networks and evolving SNN (NK, Chapters 4,5)

  8. Brain-inspired computational architectures. NeuCube software (NK, IA, Chapter 6).

  9. Evolutionary and quantum computation (NK, Chapter 7).

  10. Applications of NN and SNN for financial and economic data modeling (IA, NK, Chapter 19).

  11. Other applications of NN, DNN, and SNN. Overview of the course  (NK, WQY, IA)

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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

  1. Introduction to Neural Networks and Deep Neural Networks (DNN).

  2. Algorithms for Deep Learning 1.

  3. Algorithms for Deep Learning 2.

  4. Algorithms for Deep Learning 3.

  5. Reinforcement learning.

  6. Incremental Learning (IL) and Transfer Learning (TL).

    1. IL and TL of vector-based data in evolving neuro-fuzzy systems, exemplified by EFuNN & DENFIS in NeuCom.

    2. 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

  1. Artificial Neural Networks Fundamentals: NeuCom software.

  2. Spiking Neural Networks.

  3. Brain-Inspired Computational Architectures: NeuCube software.

  4. Evolutionary and Quantum Computation.

  5. Overview of applications of SNN for brain and genetic data modeling.

  6. Applications of SNN for streaming multisensory predictive data modeling.​

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