1

Course Introduction

A general overview of the entire course outline. Give a unified introduction to the fields, importance and frontiers involved in this course from a higher perspective. Introduction to the course paper requirements, grade evaluation, etc. for this class.

2025/2/20

2

Frontiers in Deep Learning

This topic provides an introduction to the theoretical evolution of deep learning and the applications of cutting-edge developments. It first introduces the basic paradigm of deep learning. Next, it introduces the current research frontiers in deep learning, including those in theory, algorithms, and applications.

2025/2/27

3

Frontiers in Generative Modeling

This topic introduces the research on generative models centered on diffusion models and generative adversarial networks (GAN). Firstly, it introduces the basic concepts of diffusion models and GANs and the evolution of research, and analyzes the advantages, limitations and development trends of each type of model. We show their wide applications in the fields of image generation (including text-to-image), decision making, and science. Next, the research frontiers in this field are introduced.

2025/3/6

4

Frontiers in Graph Neural Networks

This topic begins with a brief introduction to the basic concepts of graph neural networks. Next, three important classes of graph neural networks, including GCN, EGNN, and GNS, are introduced, along with their important applications in graph classification, small molecules and proteins, and physics simulation. Finally, the theoretical and applied research frontiers of graph neural networks are presented.

2025/3/13

5

Introduction to Reinforcement Learning

This topic introduces the fundamentals of reinforcement learning. It first introduces the basic concepts of reinforcement learning and deep reinforcement learning. Next, it introduces the two major branches of deep reinforcement learning: the value function-based approach, and the policy gradient-based approach. Finally, it introduces the research frontiers of reinforcement learning, including offline reinforcement learning, multi-agent reinforcement learning.

2025/3/20

6

Advanced Reinforcement learning and application in robotics and LLM

This topic introduces research on advanced topics of deep reinforcement learning (DRL), and its cutting-edge applications in robotics and Large language models (e.g., DeepSeek R1). First, it introduces various classes of model-free and model-based DRL, inverse reinforcement learning, offline reinforcement learning, and large pre-training DRL model. Then, it introduces cutting-edge applications of DRL in robotics and LLMs, such as SAC for Robot Walking, and GRPO for DeepSeek R1.

2025/3/27

7

AI + Scientific Computing

This topic will introduce the frontiers of the discipline at the intersection of AI and scientific computing. First, the foundations of partial differential equations (PDEs) will be presented, as well as a brief introduction to traditional numerical methods. Next, the basic paradigm of deep learning for modeling partial differential equations will be presented, as well as the main techniques: neural operators, graph-based simulation, ViT, etc. Finally, research frontiers in the field are presented.

2025/4/3

8

Course Project Design (1)

Each group project introduces the research problem, importance, difficulties, and major innovations proposed for their project. After the presentation of each project, for the Q&A session with the teacher's comments.

2025/4/10

9

Course Project Design (2)

Each group project introduces the research problem, importance, difficulties, and major innovations proposed for their project. After the presentation of each project, for the Q&A session with the teacher's comments.

2025/4/17

10

Foundation models

This topic first introduces the fundamentals of foundation models: the Transformer architecture. Next, it introduces the development history and major branches of big models, including GPT, BERT, etc.. Next, it introduces the development frontiers of foundation models, including multi-modal foundation models, their current limitations, and future directions, etc.

2025/4/24

11

AI + Life Sciences

This topic will introduce the disciplinary frontiers at the intersection of AI and life sciences. This includes AI for structure prediction, function prediction, and molecular design of small molecules and proteins, and AI for prediction and mechanism discovery in genomes and cells. For the above areas, the main issues in their fields, the main AI approaches, their limitations, and the research frontiers will be presented.

2025/5/8

12

Evolutionary Machine Learning and Multi-objective Optimization

This topic will first introduce the basic concepts, main methods, and research frontiers of evolutionary machine learning and multi-objective optimization. Next, it introduces its important applications in the optimization of complex engineering systems, including jet engines, Airbus airframe design, and multi-robot self-organization.

2025/5/15

13

Computer Vision, Autonomous Driving, and AI Agents

This topic introduces the foundations of computer vision, its main tasks, and the different technologies. Next, the research frontiers of computer vision in autonomous driving are presented in the context of autonomous driving. Finally, will introduce LLM-based AI Agents and their emerging application.

2025/5/22

14

Course Project Reporting and Discussion (1)

Each group project does a final presentation of the project. After each project presentation, for a Q&A session with instructor critique.

2025/5/29

15

Course Project Presentation and Discussion (2)

Each group project does a final presentation of the project. After each project presentation, for a Q&A session with instructor critique.

2025/6/5