Fundamentals of Artificial Intelligence
Introduction
This series of courses covers Python programming fundamentals, an overview of artificial intelligence technologies, and AI model training practices. From basic syntax to core concepts of machine learning, it lays a solid foundation for innovative applications of AI technology in fields such as chemistry.
Course List
- Python Fundamentals
This course introduces the basic syntax of Python, including variables and operation rules, various containers, conditional and loop statements, functions, classes, and methods concepts and definitions, with example analysis. - Introduction to Artificial Intelligence Technology
This course mainly introduces the fundamentals of artificial intelligence (AI) technology and its applications in the field of chemistry. The content covers AI concepts and terminology, including machine learning (ML) and deep learning (DL). It explains in detail the workflow of AI projects - from data preparation to model selection, training, and deployment. Additionally, the course showcases cutting-edge applications of AI in reaction prediction, spectral analysis, and material discovery, and shares commonly used AI tools and learning resources in scientific research, providing innovative ideas and technical support for chemical research. - AI Model Training
This course draws on Li Hongyi's 2021/2022 spring machine learning course content. Using Pokemon combat power prediction as an application case, it introduces various basic concepts of machine learning, including: linear models, loss functions, gradient descent, learning rates, multi-parameter gradient descent, multi-data gradient descent, polynomial fitting, overfitting and underfitting problems, and multi-layer perceptron (MLP).