Mathematical Statistics and Data Analysis

       This course covers the classic content of mathematical statistics and some methods of describing statistics and data analysis, including probability, random variables, multidimensional random variables, numerical characteristics, limit theorem, sampling survey, parameter estimation, hypothesis testing, data aggregation, two sample comparison, variance analysis, categorical data analysis, etc. It focuses on the practical application of statistics.

Random Processes

        This course is an important branch of probability theory, which is the mathematical foundation for studying the changes of random phenomena over time. It has a profound application background and can be widely applied in information science, computer science, and other engineering and technical fields. This course is mainly aimed at master's degrees in electronic information, control science, and engineering.

Matrix Theory

       This course is the foundation for studying classical mathematics and is also one of the most practical mathematical theories. It is not only an important branch of mathematics, but has also become a powerful tool for handling a large number of finite dimensional spatial forms and quantitative relationships in various modern technological fields.

Communication Theory and Systems

      This course is an important course for communication engineering and electronic information majors. Its main content includes the basic components of communication systems, the basic laws of signal transmission, and the basic technologies of communication systems. This course aims to cultivate the ability of graduate students to analyze and design communication systems, laying the foundation for researching and designing communication systems.

Modern Signal Processing Technology

        This course is a core professional course for graduate students majoring in information processing, control engineering, and other fields. The main content is the main theories, representative methods, and typical applications of modern signal processing, tracking new developments in signal processing, and mainly discussing linear signal analysis, signal detection, and signal processing theories and methods.

Analog Integrated Circuit Design

        his course is a fundamental prerequisite for students studying electronics-related fields. It provides an introduction to the fundamental structural unit of analog CMOS integrated circuits and CMOS operational amplifier content. The course objective is to develop students' abilities in analyzing and designing analog CMOS integrated circuits while ensuring they acquire the essential theories and skills for a career in microelectronics.

Linear System Theory

        This course is an important foundational course in the field of control science. "Linear System Theory" focuses on the study of linear systems and provides a comprehensive discussion of the time-domain theory of linear systems. The main topics covered include mathematical description of systems, analysis of the motion of linear systems, controllability and observability of linear systems, state-space implementation of transfer function matrices, stability of system motion, and state feedback and state observers for linear systems.

Optimal Control and State Estimation

      This course mainly focuses on learning and discussing the basic knowledge, research methods, and fundamental applications of optimal control and state estimation. It aims to enable students to systematically master the theoretical knowledge of optimal control and state estimation and possess the ability to apply this theory in basic engineering practices.

Fundamentals of Artificial Intelligence

       This course is a guiding course in the field of artificial intelligence. This course covers topics such as goal trees and rule-based expert systems, problem solving and search, game theory, machine learning fundamentals, nearest neighbor algorithms, decision trees and recognition trees, fuzzy computing and intelligence, genetic algorithms, agents, neural networks, deep learning, and commonly used networks.

Intelligent Computing Systems

       This course mainly introduces the concept of intelligent computing and its relationship with artificial intelligence. The course mainly includes three parts of intelligent computing content: evolutionary algorithms, neural networks, fuzzy computing,and its principles and methods, with MATLAB language implementation. It also introduces the concept of intelligent computing systems and the principles and implementation framework structure of deep learning.

Pattern Recognition & Machine Learning

      This course is a professional degree course in electronic information closely related to artificial intelligence. This course focuses on systematically introducing the basic theory and methods of machine learning, including: linear and nonlinear classifiers commonly used in supervised pattern recognition, unsupervised pattern recognition methods, feature selection and extraction, and evaluation of classifiers.

Algorithm Design and Analysis

        This course trains graduate students to correctly analyze the computational complexity of algorithms, select and design algorithms; to solve the increasing number and complexity of real-world application problems through the systematic construction of problem abstraction, algorithm design, and programming implementation, and to cultivate graduate students' computational thinking, algorithm design, programming, and application skills.

Introduction to Modern Optical Information Processing Technology

       This course focuses on using optical methods to implement various transformations or processing of input information. It is a newly emerging discipline developed in recent years, based on holography, optical transfer function and laser technology. The Fourier transform effect of lenses is the theoretical core of optical information processing.