Course

This course will introduce a number of forward-looking optimization technologies. In order to meet the SOC vision, we will focus on the mathematical principles behind it, and focus on real-world applications. This course will mention satellite image processing, medical data analysis, 5G wireless communication, metasumber design, etc., and take these most forward-looking technologies and applications as examples. We will introduce the bridge between these basic mathematics and real-world applications in a straightforward way.

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This advanced semiconductor course teaches the device structure and physics, process technology, basic circuit architecture and design principles. The course content includes volatile memories such as SRAM and DRAM, conventional flash memory including NAND and NOR, and emerging non-volatile memories RRAM, PCM, MRAM and FeRAM

Listed in MS Degree in Intelligent Computing、Intelligent Computing Industrial Doctorate Program

This course will aim on the introduction to semiconductor manufacturing technology, starting from the fundamental materials properties to full process integration flow of electronic devices. The content includes the device characteristics of the integrated circuit and the process steps include diffusion, photolithography, etching, thin film deposition and chemical-mechanical polishing, which form the cornerstone of this course. The final part involves the advanced semiconductor manufacturing processes including FinFETs, GAAFETs and so on.

Listed in MS Degree in Intelligent Computing、Intelligent Computing Industrial Doctorate Program

This is an introductory course in the theory of computation, using mathematical concepts and methods to understand the nature of computational phenomena. The theory explains the creation, functions, and limitations of virtually any computer that exists or may be constructed in the future. This course also introduces the basic theory of computational complexity, with a special discussion of its relationship to complexity-based cryptography. Computational complexity theory utilizes the Turing machine as a mathematical model for computers to study the resources time and space required to solve certain problems with computers. By identifying difficult and intractable problems that cannot be solved by efficient algorithms, computational complexity theory provides the basis for modern cryptography, which aims to design cryptosystems that are difficult to break with limited resources. The course also covers quantum computing, the latest breakthrough in cryptography. The mathematical formulation of quantum computation can be obtained naturally by extending classical computation with the concept of linear algebra. These extensions include quantum gates and quantum wizards, which can be used to model parallel computation using quantum superpositions with the goal of speeding up computation time using novel algorithms.

Listed in Intelligent Computing Credit Program

1: Supervised versus unsupervised learning 2: Multilayer Perceptron 3: Logistic regression 4: Learning parametric estimation 5: Probabilistic approximately correct 6: Common Machine Learning Models 7: Regularized learning 8: Self-supervised Contrastive Learning 9: Support Vector Machine 10: VC-dimension on the learning 11: Learning bound analysis

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Computational modelling, AI techniques and data analytics have been widely used in the fields of brain research and cognitive psychology. The goal of this course is to introduce students to basic concepts and topics in cognitive psychology based on a computational approach. The course will cover language, reading, learning, memory, vision, and brain activity. All topics will be presented from a combined view of experimental designs and computational modelling especially how computational models can simulate experimental data and reveal processing mechanisms underlying cognitive functions and what their potential applications are.

Listed in Intelligent Computing Credit Program

CAPSTONE Seminar is based on the principle of cross-disciplinary student composition as well as relevant teachers provide consultation and discussion, allowing students to complete the integrated application in the form of free propositions. All teams will demonstrate their research in the exhibition at the end of every semester. Teachers and students participating in the application of intelligent computing technology to cultivate students' bilingual ability, professional quality and practical skills, and at the same time emphasize the cultivation of soft power such as needs analysis and communication, teamwork, social care and integration of software and hardware systems to strengthen students practical ability and performance.

Listed in Intelligent Computing Credit Program

The course is divided into two parts. The first part covers the use of generative AI in various professions and scenarios. Students will work in groups for role-playing exercises. The second part focuses on the application of generative AI in quantitative research in the social sciences. Students will replicate relevant papers and engage in critical discussions. The course concludes with students presenting a final paper evaluating the impact of generative AI applications and proposing new analysis methods and frameworks.

Listed in Intelligent Computing Credit Program

This course provides an introduction to basic probability models and statistical methods. This includes descriptive statistics, elementary probability ideas and random variables, distributions of sample averages. In addition, the more advance statistical inference approaches such as analysis of variance ANOVA and regression will be covered in the course. Students learn the conceptual underpinnings of statistical methods and how to apply them to address more advanced statistical questions with real examples. We make use of some light programming in R, for the purposes of simulation and data analysis.

Listed in Intelligent Computing Credit Program

This is an introductory hands-on course of AI and robotics. This course consists with three parts. The first part deals with very basics of image recognition AI with deep learning. Students will learn how to build, train, and evaluate deep neural network models by writing Python codes using deep learning frameworks such as PyTorch. The second part deals with implementation of an image recognition AI into robotics. By playing with a small robot car such as NVIDIA JetBot, students will learn how an image recognition AI can be used for controlling robots that interact with the real world. Students can also acquire many know-hows and techniques regarding AI and robotics which are necessary for conducting other projects. In the third part, the students will work on their own mini projects. By combining AI and some other hardware or software, the students will develop something new or useful or fun etc. with their own idea. We will go through some related techniques useful for carrying out the projects such as other Python packages and how to use sensors and actuators. Finally, students will present their own projects. By finishing this course students will be able to: • develop an image recognition AI with a custom dataset. • explain how image recognition AI can be used for controlling robots or hardware. • propose and carry out an AI project.

Listed in Intelligent Computing Credit Program

The course's main objective is to introduce and apply methods of general interest in modelling and simulations. This course focuses on computational methods and applications relevant to biomedical engineering within diagnostic and therapeutic applications as well as for physiological processes. This course will provide students with the modelling concepts of biomedical engineering and understand the fundamental methodology via the computational framework. During this lecture, we will mix theory and hands-on practice in relevant application areas. In addition, this course will introduce students to how these modellings can be integrated into biomedical engineering applications and tools.

Listed in Intelligent Computing Credit Program

This course introduces the programming language Python, from basic usage to advanced applications. Python is a powerful object-orientated programming language, which can be used in different areas, such as network programming, image processing, robot control, and deep learning. This course aims to develop the ability to solve the problems in real life and at work by using python.

Listed in Intelligent Computing Credit Program