National Taiwan University

This course takes students on a journey through one of artificial intelligence's most dynamic fields. Deep reinforcement learning (DRL) has achieved remarkable breakthroughs, from mastering complex games to controlling robots. The course discovers how artificial intelligence (AI) agents learn to make decisions through interaction, beginning with core concepts in reinforcement learning and deep learning; then it explores how these powerful approaches combine to create sophisticated learning systems. 
 
The course progresses naturally through key topics in decision making with Markov processes, modern deep learning techniques for AI, value-based methods that help agents evaluate their choices, policy optimization approaches for learning effective behaviors, and advanced strategies for stable and efficient learning. The course emphasizes practical understanding through hands-on examples.  By the end of the course, students will understand how to build AI systems that can learn and adapt in complex environments. 
 

This course explores the Anthropocene, a proposed geological epoch marked by significant human impact on Earth's ecosystems, climate, and geology. By focusing on pluralizing the Anthropocene, the course examines a diverse range of perspectives, including postcolonial, feminist, indigenous, and ecological frameworks.  The course interrogates how the term 'Anthropocene' can be problematized and expanded, reflecting on how different cultures, knowledge systems, and disciplines engage with the concept.  

This course explores the idea of the Anthropocene in two parts: 

(1) Theory and Concept: The course analyzes how scholars define the Anthropocene with different theoretical backgrounds, providing diverse understanding of nature-social and human-nonhuman relations. 

(2) Case studies: The course dives into various case studies to learn how geographers and anthropologists adopt diverse methods to study the uneven impacts of the Anthropocene across the world.  

This course focuses on the introduction of dynamic modeling and its application for policy analysis. The aim is to provide an understanding of the reasons for government policy intervention in the economy; analyze the benefits of possible government policies, and the response of economic agents to the government's actions. The course covers tax policy and inequality, social insurance programs, and public goods. Special emphasis is on current policy issues such as inequality and poverty, health care reform, income tax reform, and budget deficits. 

The course is designed for senior and graduate students majoring in Computer Science to learn design philosophy, practice, and research challenges for software design for smart medical sensing systems.   

Smart sensing systems have the capability of processing the sensing data on the device and the capability of providing the detected events as the outputs. This type of sensing system is required to generate accurate sensing events in real time. The systems are also required to minimize their energy consumption in specific application scenarios.  With smart sensing systems, the faults can be contaminated, the system can be more robust and easier to develop. Finally, the systems can be certified for medical use.   

This course covers model smart sensing devices, realtime computation, Computing-In-Memory devices, and communications between computing devices.  

This course surveys US law governing mergers and acquisitions and analyzes the agreements lawyers use to initiate and complete these transactions. The course covers structures commonly used in M&A transactions; the duties of management and directors; the rights of shareholders; and the structure and important terms of acquisition agreements.   

Transnational corporations and local businesses face significant challenges arising from geopolitical tensions. This course introduces the intricate relationship between geopolitics and business operations at both the international and local levels.

How does cancer evolve in a patient? This course looks at cancer from an evolutionary angle and provides another way of thinking about cancer biology.  

This course provides an opportunity to appreciate the beauty of biodiversity from lecture instructor lectures and from field work in the NTU Experimental Forest. The course invites multiple instructors to provide general knowledge of biodiversity, sustainable development goals and the idea of 30 by 30. The course includes a five-day field trip to the NTU Experimental Forest where students conduct hands-on experiments with the instructors and discuss biodiversity management with the local community. 

This course introduces the basic principles of toxicology: the physical and chemical properties of poisons and their distribution processes (absorption, distribution, metabolism, and excretion); Toxicokinetics, and Toxicodynamics. The course also discusses the effects on toxic distribution, toxic reactions and mechanisms, and then the distribution of chemical substances in the environment. Finally, the course explains emerging technologies applied in toxicant metabolism and environmental toxicology, which is accompanied by a laboratory tour. 

This graduate-level course prepares students for theoretical research in financial markets and is based on journal articles and working papers. The first part of the course discusses different methods to facilitate transactions. The course introduces secured and unsecured credit, and compares the difference between credit and money. The second part of the course discusses search and matching friction in the financial markets, discussing models applying search and matching friction to study stock markets, housing markets, and bond markets. The last part of the course discusses the current development of fintech, introducing the theory behind cryptocurrencies and analyzing how digital currencies influences the current economy.