Artificial Intelligence: Algorithms and Optimization in Robotics, Game Design and Network Security

Course 14: Artificial Intelligence: Algorithms and Optimization in Robotics, Game Design and Network Security

I. Course Description

This course will focus on the basic knowledge of artificial intelligence (AI) and deep learning, covering convolutional neural networks (CNN), graph neural networks, generative neural networks, and converters. In class, through examples and discussions, we will show students the application of AI in different fields such as graphics, audio and text analysis, economics, robotics, cybersecurity, health and games. The curriculum is designed to help students develop research skills and have a clearer understanding of AI. Through the course, students are able to use various libraries and tools and prepare for the real challenges and opportunities in these dynamic areas.

This course aims to make a comprehensive introduction of the core technology and application in the field of artificial intelligence, covering data warehouse, image, audio and text analysis of AI technology, Transformer model and attention mechanism, large language model (LLM), probability diffusion model, can explain artificial intelligence (XAI), and reinforcement learning and deep reinforcement learning and frontier topics. Through the combination of theory and practice, students will master the application of AI in multimodal data processing, intelligent decision-making and network security, develop the ability to solve practical problems, and have a deep understanding of the principles and techniques of AI models, especially in ensuring transparency and interpretability.

II. Professor Introduction

Pietro Liò – Tenured Professor at Cambridge University

Pietro Lio Professor He is currently a tenured professor in the Department of Computer Science and Technology of the University of Cambridge, the head of the Computational Biology Research Group, and also a core member of the Artificial Intelligence Research Group and the Medical Artificial Intelligence Center of the University of Cambridge. H-index72, the paper has been cited for 5W +. His papers have been published in ICML and the top journals and conferences of Nature and IEEE.

Professor Lios research interest has focused on developing AI and computational biology models to understand the complexity of disease and drive the development of personalized and precision medicine. Currently, he focuses specifically on the study of graph neural network models. Professor Lio holds a masters degree from the University of Cambridge and a PhD in Complex Systems and Nonlinear Dynamics from the School of Informatics at the University of Florence and a PhD in Theoretical Genetics from the University of Pavia. He is also a member of the European Laboratory for Learning and Intelligent Systems (ELLIS) and the European Academy of Sciences, and has been listed on its list of top national scientists. At the same time, Professor Lio holds important positions in several academic organizations and committees at the same time, has made outstanding contributions to the fields of artificial intelligence and computational biology, enjoys a high reputation in academia, and continues to make important contributions to promoting the research of personalized and precision medicine.

III. Syllabus

1. Introduction to machine learning
2. supervised learning
3. unsupervised learning
4. deep learning
5. Basis of network security
6. Advanced network security
7. Introduction to the Large Language Model (LLM)
8. In-depth exploration of LLM
9. Model interpretability
10. For applications in games and robotics