Today, computing power drastically increases as technology evolves. AI is considered as one of possible solutions to the problems that human beings are facing now. In addition, emerging memories have some important features that can emulate the working mechanisms between neurons and synapses. Hence, they can be the building blocks for both computing-in-memory and neural networks. So, in this course, students will learn memory technology, neural networks and the combination of these two technologies to achieve AI.

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This course will start from the brain structure and function to neuron transport mechanism. In addition, combined with hardware design and semiconductor devices, the neuromorphic computing can be realized by implementing neuron transport functions to semiconductor chips. Not only introduce how brain works, but also cover the semiconductor device and memory device working mechanisms. Students can learn the concepts of neuromorphic computing chips and its progress.

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Machine Learning is a popular research area lately. The main goal is to investigate the high dimensional, large amount or complex data, and to develop the useful algorithms to discover information within data.

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This course will provide mathematical foundations of intelligent computing, including, but not limited to, calculus, linear algebra, numerical calculation, and machine learning. The course content includes, but is not limited to, the concepts of multivariate extreme values, the gradient method, matrix equations, eigenvalues, the least square method, and the classification method. We, in this class, intend to offer students mathematical concepts of intelligent computing and the corresponding algorithm implementations so that our students have the chance to prepare for further research in this area.

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The course introduces some of mathematical foundation and skills used to analyze various discrete structures inherent in computing. This foundation is indispensable for students' further study in computer science and information technology. The course emphasizes the key ideas and motivation for the mathematical objects and methods, while at the same time strikes a balance between theory and application to solve practical problems for computing. The course aims to lead a mastery of key threshold concepts in mathematical foundation of computing.

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This course gives a basic overview of an AI chip architecture and system design. To prepare everyone for learning AI chip design, some hardware design and software design foundations are introduced subsequently. Lab sections are included over the first 10 weeks of the course to help students learn hands-on skills. A final capstone project is designed to apply what is covered in the lectures and dive into in-depth ideas for specific topics proposed by students. This course is recommended for those who like to learn AI chip design from scratch.

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This course will introduce several advanced optimization technologies. To conform to the vision of SOC, we will emphasize the mathematical principles behind, and pay great attention to real-world applications. We will talk about applications, including satellite image processing, biomedical data analysis, 5G wireless communications, as well as metamaterial design, etc. With these cutting-edge technologies and applications, we will illustrate in simple language how the math fundamentals are bridged to real-world applications.

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This course introduces the programing language Python, from basic usage to advanced applications. Python is a powerful object-orientated programing language, which can be used in different areas, such as network programing, 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.

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This course first gives an overview on the AI applications and how AI computing systems are built in these applications. To prepare everyone for learning interdisciplinary knowledge, some mathematical and computer sciences foundations are introduced subsequently. Python review sections are included in the first few lectures to help students recap python programming skills. It then explores the concepts and algorithms at the foundation of modern artificial intelligence, diving into the ideas that give rise to technologies like game-playing engines, handwriting recognition, and machine translation. Through 6 Raspberry PI hands-on projects, students gain exposure to the theory behind graph search algorithms, classification, optimization, reinforcement learning, and other topics in artificial intelligence and machine learning as they incorporate them into their own Python programs. The course will end with a series of discussion on AI issues and applications.

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This course covers the law and policy implications and ramifications of the developments in AI and data-driven society. Via weekly readings and discussions, this course aims at equipping students with a general background of AI law and policy, including the toolkit of accountability, transparency and explainability, fairness and equality, safety and security, data privacy, human rights. The focus of this course will be, inter alia, the rising concerns of democracy and rule of law in Taiwan today.

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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.

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Algorithms are crucial to artificial intelligence, and have been deeply relevant to fields such as social science, linguistic analysis, statistics, data analysis, computer science, information engineering, etc. The more agile the deployment of an algorithm, the more efficient it would be to execute tasks. As we learn more and more algorithms , it soon comes the question of deciding what algorithm to use. Complexity theory is important in regard to choosing the best algorithm to use. In this course, Professor Hamano starts with the Theory of Computation, where complexity theory is derived from and with mathematical concepts and methods. Turing Machine modelling, deterministic and non-deterministic functions are in the following classes, including extensions of Turing Machine, cryptography and quantum computation as well.

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The first international joint course in SOC Miin Wu School of Computing has collaborated with National Institute of Technology, Kagawa College, Japan NITKC, to design the syllabus and instruct students in the course “Introduction to Image Recognition AI and Robotics Lab”. Starting with basics of image recognition AI, students will learn how to build, train and evaluate a deep learning model, and eventually apply the model on robots. NCKU students will take the class with NITKC students together remotely, and work on an AI robot project. The course provides an opportunity for students to have hand-on experiences. In addition, there might be a field tripin-person visit to NITKC under safe conditions.

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Stochastic Process is the foundation for a number of stochastic modellings, such as Poisson, Gaussian, queuing models etc. Stochastic models are widely applied in mechanical engineering, electrical engineering, computer science, system engineering, social science, financial management, computer vision, deep learning and other fields. This course introduces a basic theory of stochastic process at the beginning, and then focuses on the theory of discrete and continuous Markov processes. In the recent past, the stochastic process was frequently used to optimize algorithms and models. The objective of this course is to instruct students deploying the models efficiently to complete the research and execute tasks in a better way.

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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.

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To run an AI model on the target hardware system, there are multiple options to establish the software stacks. Depending on the performance requirement of the target system, the software stack might include a OS and a runtime library. The goals of the software stacks include: • Running inferences of pre-trained neural network models in a list of supported formats • Integrating with the hardware emulator QEMU for AI accelerator modeling • Enabling software/toolchain development and research This course will provide students an opportunity to choose a target system and create a system software stack for AI model inference on the target system.

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The seminar provides students with an opportunity to discuss ongoing research in Explainable AI. At most sessions, an invited speaker from NCKU or elsewhere will present work in progress, and then take questions from students and faculty in the audience. Speakers include scholars in the field of law, psychology, and computer science.

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Explainable artificial intelligence XAI is an important emerging area of research in the field of AI because explainablity is essential especially for biomedical and health care applications. This class will provide an overview of the relevant XAI techniques e.g., XAI classification, posthoc and transparent methods for biomedical and health care applications. In combination with hands-on laboratory, the students are expected to develop hands-on skills and solid knowledge in explainable artificial intelligence.

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Evolutionary Computation is powerful and broadly applicable stochastic search and optimization techniques based on principles from evolution theory. In the past few years, the evolution programs community has turned much of its attention toward the optimization problems of industrial engineering, resulting in a fresh body of research and applications that expands previous studies. The lecture will include the theory and application of related areas in evolutionary and natural computation centering on Genetic Algorithms and programming, evolution strategies, artificial life, and other models that rely on evolutionary principles. Students will perform course projects that apply the discussed techniques to numerical optimization problems, machine learning, and to the simulation of biological and cultural systems.

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Special topics on intelligent control

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Miin Wu School of Computing invites Taiwan AI Academy to provide a short course in "AI Accelerator", and the instructor will be Dr. HT Kung. Dr. HT Kung is William H. Gates Professor of Computer Science and Electrical Engineering at Harvard University. He is a member of National Academy of Engineering and also a member of Academia Sinica in Taiwan. Students will learn the future trend of AI accelerator's architecture, software and hardware. In the short future, Miin Wu School of Computing will provide more world-leading lectures.

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