This course is a foundational course in Artificial Intelligence offered by the School of Computer Science at Beijing Institute of Technology, taught by Professor Che Haiying. It uses a mix of Chinese and English teaching and has a depth and set of requirements higher than general AI courses. The course takes a computational science perspective, systematically explaining the underlying principles of AI, focusing on the three core elements of data, computing power, and algorithms, and integrates basic knowledge from computer science, information science, and intelligent science to build a complete AI computational thinking framework.
The course first reviews the development of AI and the core ideas of its three major schools of thought, covering foundational computational theories such as information encoding, information theory, algorithm complexity, and high-performance computing. Building on this, it systematically teaches traditional AI methods like symbolic intelligence, heuristic search, knowledge representation, and logical reasoning. It also emphasizes modern AI core technologies, including supervised and unsupervised machine learning, basic neural networks, reinforcement learning, and swarm intelligence, illustrated with cutting-edge examples like large models, intelligent systems, and autonomous competition scenarios.
The course balances theoretical foundations with practical engineering applications, using research and industry case studies to explain the computational constraints, performance costs, and implementation logic of AI technologies. Students also engage in programming exercises, literature reviews, and group project discussions to develop their ability to analyze intelligent systems and solve AI engineering problems. Additionally, the course covers AI ethics, security risks, and industry standards to guide students in forming a responsible and rigorous approach to applying AI technologies.
The aim of the course is to help students grasp the complete theoretical framework and computational logic of AI, develop the ability to independently learn advanced AI technologies, and conduct basic intelligent system design and analysis, laying a solid foundation for subsequent professional courses in machine learning, deep learning, big data analysis, and intelligent system development.
