This course offers a comprehensive introduction to large language models (LLMs) in generative AI. Topics include: (i) Fundamental principles of LLMs—from pre-training to reinforcement learning from human feedback (RLHF); (ii) Core techniques supporting LLM training and inference, such as scaling laws, supervised fine-tuning, and safety measures; (iii) Practical applications using publicly available LLMs and APIs across various domains.

This course provides a comprehensive introduction to deep reinforcement learning, equipping you with the knowledge and practical skills to launch research and real-world applications. Topics include: (i) Fundamental reinforcement learning algorithms, covering Markov decision processes, dynamic programming, and planning techniques; (ii) Core deep RL methods such as DQN, continuous control, imitation learning, offline reinforcement learning, and Control as Inference; (iii) Advanced topics and applications, including model-based reinforcement learning, world models, and diverse applications spanning robotics, game AI, multi-agent systems, bioinformatics, molecular design, ad optimization, physical simulation, traffic engineering, and finance.

This course offers a comprehensive introduction to developing financial trading algorithms using machine learning. Topics include: (i) Fundamental financial market concepts and technical analysis; (ii) Essential techniques for algorithm development such as dataset creation, labeling, and backtesting; (iii) Practical implementation of quantitative trading strategies through hands-on exercises and a competitive final project.

This course explores business trends across various industries and solutions based on insights from AI research and social implementation, examining the impact of AI on society and business. Topics include: (i) Practical skills for leveraging AI in business management; (ii) Latest AI application case studies from instructors at the forefront of business; (iii) Organizational strategies for implementing AI in business operations. Participants will understand both the potential and risks of AI utilization, learning offensive and defensive approaches to effectively incorporate AI into corporate strategy.