Fundamentals of Artificial Intelligence
Introduction
This course series covers introductory Python programming, an overview of artificial intelligence (AI) technologies, and hands-on practice in AI model training. Spanning from basic programming syntax to the core concepts of machine learning, it establishes a solid foundation for innovative AI applications in chemistry and related fields.
Course List
- Introduction to Python
This course introduces the fundamentals of Python syntax, including variables and operations, data containers, conditional and loop statements, as well as the concepts and definitions of functions, classes, and methods. Each topic is accompanied by illustrative examples to facilitate understanding. - Introduction to Artificial Intelligence Technologies
This lecture provides an overview of AI and its applications in chemistry. It covers fundamental concepts and terminology, including machine learning (ML) and deep learning (DL), and explains the typical workflow of an AI project—from data preparation to model selection, training, and deployment. The lecture further highlights cutting-edge applications of AI in chemical reaction prediction, spectral analysis, and materials discovery, while also introducing commonly used research tools and learning resources that support innovation in chemical sciences. - AI Model Training
Drawing inspiration from Hung-Yi Lee’s 2021/2022 Spring Machine Learning course, this module introduces essential machine learning concepts through a practical case study of predicting Pokémon combat power. Topics include linear models, loss functions, gradient descent, learning rates, gradient descent with multiple parameters, polynomial fitting, overfitting and underfitting, as well as multilayer perceptrons (MLPs).