Master of Science in Intelligent Technology

Course Description

Core Courses

Intelligent Control Technology (3 credits)

This course represents a new stage in the development of control theory and is primarily used to address complex control problems in systems that are difficult to solve using traditional methods. Intelligent control systems, with intelligent control as the core, it exhibit certain intelligent behaviors such as self-learning, self-adjustment, and self-organization. The main topics covered in this course include the fundamental concepts of intelligent control, the foundations of fuzzy control theory, fuzzy control systems, artificial neural network models, neural network control theory, and integrated intelligent control systems.

Optimization Methods (3 credits)

This course focuses on studying modeling methods and efficient problem-solving techniques for various engineering optimization problems. The main topics covered in this course include linear programming, nonlinear programming, integer programming, network programming, dynamic programming, various intelligent optimization methods, optimization problem modeling, and its applications.

Fundamentals of Artificial Intelligence (3 credits)

This course will introduce a broad overview of AI and the history of AI. The course covers the following main topics of artificial intelligence: searching by problem space, reasoning by knowledge, planning by rules, learning by data (machine learning and reinforcement learning), and applying. The applying subject can be divided into three categories, communicating (e.g., NLP, Machine Translation), perceiving (e.g., Vision, Speech, Sensing), and acting (e.g., Robot).

Numerical Analysis (3 credits)

This course analyzed the basic techniques for the efficient numerical solution of problems in science and engineering. Topics spanned root finding, interpolation, approximation of functions, integration, differential equations, and direct and iterative methods in linear algebra. Students can understand the basic discipline of numerical analysis, be able to carry out the classical numerical methods for some mathematical problems, and have the basic knowledge and skills to use computers to solve mathematical problems and conduct research by both algorithm designing and computer programming. This course aims to provide students with a strong foundation that will benefit their future studies and jobs.

Introduction to Statistics (3 credits)

This course mainly explains the basic concepts, theories, and methods of statistics. The main content includes the basic framework of statistics, organization, collection, and presentation of statistical data, description of data distribution characteristics, probability foundations, parameter estimation, statistical decision-making, statistical synthesis evaluation, and the application of Excel in statistics. It involves quantitative analysis and summarization of collected observational data and provides a basis and reference for related decision-making.

Elective Courses

Introduction to Management (3 credits)

This course is the study of the general laws of management theory, methods, and practical activities. This course introduces the basic knowledge and principles of management, such as planning, organizing, leading, and controlling. It also covers professional management topics such as strategic management, production and operations management, marketing management, information management, logistics management, human resource management, and financial management.

Introduction to Internet of Things (3 credits)

This course mainly introduces the basic concepts and technologies of the Internet of Things, including radio frequency identification technology, smart sensing technology, network collaboration technology, and service optimization technology.

Pattern Recognition (3 credits)

This course refers to the process of processing and analyzing various forms of information representing objects or phenomena, in order to describe, identify, classify, and interpret them. This course discusses Bayesian classification, Bayesian networks, design of linear and non-linear classifiers, dynamic programming, hidden Markov models for sequential data, feature generation, feature selection techniques, the fundamental concepts of learning theory, and clustering concepts and algorithms.

System Simulation (3 credits)

This course provides a detailed introduction to the basic concepts, principles, and methods of system simulation. It also explains the basic methods of computer implementation techniques. The main content includes the basic concepts and principles of system simulation, fundamental mathematical theories in simulation, detailed introduction to modeling methods for discrete events, pedestrian modeling methods, and logistics system simulation.

Introduction to Algorithm Theory (3 credits)

This course is a course that studies algorithm design and analysis of algorithm complexity. The main content includes various basic algorithm design methods, methods for analyzing algorithm complexity, problems that can be solved using polynomial-time complexity, algorithm complexity theory, NP-hard problems, proofs of NP-hard problems, and approximate solution methods.

Fuzzy Systems (3 credits)

The main contents introduced in this course include fuzzy set theory, fuzzy logic, fuzzy reasoning, and fuzzy control.