SOC

Artificial Intelligence and Memory|Course taught in Chinese

Instructed by
Chao-Hung Wang

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.

Listed in Intelligent Computing Credit Program

Introduction to Neuromorphic Computing|Course taught in Chinese

Instructed by
Chao-Hung Wang
Ya-Ning Chang

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.

Listed in MS Degree in AI Robotics、MS Degree in Intelligent Computing、Intelligent Computing Credit Program

Statistical Prediction and Machine Learning|Course taught in Chinese

Instructed by
Ray-Bing Chen

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.

Listed in Intelligent Computing Credit Program

Mathematical Foundation of Intelligent Computing|Course taught in Chinese

Instructed by
Matthew M. Lin
Yu-Chen Shu
Chern-Shuh Wang
Min-Hung Chen
Dean Chou

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.

Listed in Intelligent Computing Credit Program

Mathematical Reasoning and Computation|English

Instructed by
Masahiro HAMANO

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.

Listed in Intelligent Computing Credit Program

AI Computing Architecture and System|Course taught in Chinese

Instructed by
Wei-Fen Lin

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.

Listed in Intelligent Computing Credit Program

Optimization Theory and Application|Course taught in Chinese

Instructed by
Chia-Hsiang Lin

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.

Listed in MS Degree in AI Robotics、MS Degree in Intelligent Computing、Intelligent Computing Credit Program

CAPSTONE專題實作二|Course taught in Chinese

Instructed by
Curtis, Jin-Yi Wu
Shao-Man Lee
Chao-Hung Wang
Alan Yi-Yu Hsu
Ya-Ning Chang
Yu-Chen Shu

這門課是智慧運算學分學程的頂石課程,由敏求智慧運算學院的老師密切陪伴、引導同學進行智慧運算跨域應用專題。希望讓同學在學習智慧運算相關課程後,能進一步挑戰運用這些能力,解決社會實際問題。這堂課分成一與二兩部份。第一部分將讓同學從尋找定義問題開始,應用所學完成跨域整合應用專題初步構想,並成為下學期第二部分的專題的敲門磚。

Listed in Intelligent Computing Credit Program

動態網頁程式設計|Course taught in Chinese

Instructed by

本課程教授server side與client side網頁程式技術與程式語言。並介紹網頁開發工具和伺服器,使學生能開發出心目中的網站。

Listed in Intelligent Computing Credit Program

Python Programing|Course taught in Chinese

Instructed by
Curtis, Jin-Yi Wu

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.

Listed in Intelligent Computing Credit Program

Ai, Computing And Applications|Course taught in Chinese

Instructed by
Wei-Fen Lin

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.

Listed in Intelligent Computing Credit Program

Artificial Intelligence: A Law & Policy Perspective|Course taught in Chinese

Instructed by
Shao-Man Lee

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.

Listed in Intelligent Computing Credit Program

Introduction To Probability And Statistics In Data Science|Course taught in Chinese

Instructed by
Kuo-Jung Lee

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

Introduction to Theory of Computation|English

Instructed by
Masahiro HAMANO

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.

Listed in Intelligent Computing Credit Program

Introduction To Image Recognition Ai And Robotics Lab|English

Instructed by
Alan Yi-Yu Hsu

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.

Listed in Intelligent Computing Credit Program

Stochastic Process|English

Instructed by
Masahiro HAMANO

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.

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

Computational Cognitive Science|Course taught in Chinese

Instructed by
Ya-Ning Chang

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

AI Chip System Software Capstone Project|Course taught in Chinese

Instructed by
Wei-Fen Lin

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.

Listed in Intelligent Computing Credit Program

Explainable AI Seminar|Course taught in Chinese

Instructed by
Shao-Man Lee

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.

Listed in MS Degree in AI Robotics

Exploring explainable AI in biomedical data science|English

Instructed by
Alan Yi-Yu Hsu
Ya-Ning Chang

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.

Listed in MS Degree in AI Robotics

Evolutionary Computation|Course taught in Chinese

Instructed by

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.

Listed in MS Degree in AI Robotics

AI Accelerators|Course taught in Chinese

Instructed by

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.

Listed in MS Degree in AI Robotics、MS Degree in Intelligent Computing、Intelligent Computing Industrial Doctorate Program、Intelligent Computing Credit Program

智慧運算導論|Course taught in Chinese

Instructed by
Masahiro HAMANO
Curtis, Jin-Yi Wu
Shao-Man Lee
Chao-Hung Wang
Alan Yi-Yu Hsu
Ya-Ning Chang

這堂課是為無智慧運算基礎背景的同學開設,歡迎成大全校學生選修。課程旨在透過兩周的密集課程訓練,讓同學建立程式、影像處理、倫理政策、自然語言分析、認知科學、記憶體、半導體等基本認知與能力,以理論與實作並行的方式,探索智慧運算各個面向。 本課程為彈性密集課程,於8/15-8/26授課。外系所申請方式及每日上課時間請見課程大綱。

Listed in MS Degree in AI Robotics、MS Degree in Intelligent Computing、Intelligent Computing Credit Program

運算思維與解決問題|Course taught in Chinese

Instructed by
Wei-Fen Lin

這門課程教授運算思維以及提供大量實例分析訓練,讓學生在課程結束後掌握解決問題的一套流程,透過定義、拆解問題、推論歸納規律性與抽象化等觀念和技巧,判斷與轉換適合用運算思維處理的問題來解決難題。 學生會在期末使用課堂所學到的運算思維以及解決問題的技巧來練習解決生活實際遇到的問題。本課程適合任何想要學習如何透過程式設計來解決問題的學習者,沒有學過程式設計者也可以選修。本課程採報名審核選課制,詳情請查看下方課程大綱。

Listed in Intelligent Computing Credit Program

計算理論導論|English

Instructed by
Masahiro HAMANO

演算法是人工智慧發展至今重要的一環,亦已深入至許多領域的研究與應用,如社會科學、語言分析、統計學、數據分析、資訊工程、電腦工程等。靈活應用演算法能加速執行任務的效率。然而要了解演算法的效率,便需要了解「複雜度理論」。 這堂課屬於入門級別,從「複雜度理論」的根源——計算理論(Theory of computation)談起,講解圖靈機模型(Turing Machine)及其所衍生的決定性(deterministic)與非決定性(non-deterministic)函數,乃至複雜度理論。 這堂課亦會談到與複雜度理論相關的密碼學及量子電腦,讓同學對未來資訊安全及未來電腦革新有基本的認識。

Listed in Intelligent Computing Credit Program

數學推理與演算法|English

Instructed by
Masahiro HAMANO

數學推理是撰寫演算法及程式的關鍵。本課程希望讓同學掌握運算數學基礎中的關鍵核心:「離散數學」。它對電腦科學、資訊科技,乃至人工智慧的應用都非常重要,可以說是電腦科學中的數學語言。這堂課著重介紹用於分析運算過程中,各種「離散數學」的基礎和技巧,亦會在理論和應用之間取得平衡,進而引導同學試著解決實際問題。

Listed in Intelligent Computing Credit Program

智慧分析與數學建模於生醫應用|Course taught in Chinese

Instructed by
Yu-Chen Shu
Dean Chou

人工智慧為生物醫學工程領域帶來極大的變革;在未來,亦將持續加速生物醫學工程等領域發展。本課程結合理論與實踐,帶給學生生物醫學工程相關建模和模擬中感興趣的方法及其應用。透過本課程,同學可以了解到該領域相關應用知識,以探索未來發展可能性。

Listed in Intelligent Computing Credit Program

CAPSTONE專題實作一|Course taught in Chinese

Instructed by
Masahiro HAMANO
Curtis, Jin-Yi Wu
Shao-Man Lee
Chao-Hung Wang
Alan Yi-Yu Hsu
Ya-Ning Chang

這門課是智慧運算學分學程的頂石課程,由敏求智慧運算學院的老師密切陪伴、引導同學進行智慧運算跨域應用專題。希望讓同學在學習智慧運算相關課程後,能進一步挑戰運用這些能力,解決社會實際問題。這堂課分成一與二兩部份。第一部分將讓同學從尋找定義問題開始,應用所學完成跨域整合應用專題初步構想,並成為下學期第二部分的專題的敲門磚。

Listed in Intelligent Computing Credit Program

人工智慧與治理|Course taught in Chinese

Instructed by
Shao-Man Lee

當代社會已經進入巨量資料時代,大量數位資料的積累,以及人工智慧的發展,已經開始改變民主治理的樣貌。民主治理的討論視野,從過往以政府為中心,逐漸觸及公私部門的治理行動、過程、與社會實踐。本課程的目標,在於耙梳近年人工智慧與民主治理的發展。課程將涵蓋民主治理的重要概念,並引導學生深入探討特定民主治理議題並進行實作。

Listed in MS Degree in AI Robotics、MS Degree in Intelligent Computing、Intelligent Computing Credit Program

人工智慧晶片系統專題一|Course taught in Chinese

Instructed by
Wei-Fen Lin

本堂課以專題討論的形式進行,希望藉由解決實際問題,讓同學可以練習開源軟體專案實作與整合,以及人工智慧晶片系統軟體架構與設計。學生將在人工智慧晶片系統軟體領域,挑選一主題進行實作與討論。本課程採報名審核選課制,詳情請查看下方課程大綱。

Listed in Intelligent Computing Credit Program

人工智慧晶片系統專題二|Course taught in Chinese

Instructed by
Wei-Fen Lin

本課程設置旨在培育系統單晶片設計與驗證的人才,帶領學員認識 FPGA 相關設計及實現流程,並使用Synopsys原型驗證(Prototyping)解決方案,從完整的原型驗證流程(Prototyping Flow)中,瞭解RTL 從 bitstream 到 System-level的除錯(debug)功能。

Listed in Intelligent Computing Credit Program

自然語言處理|Course taught in Chinese

Instructed by
Alan Yi-Yu Hsu
Ya-Ning Chang

自然語言處理 NLP 是人工智慧領域一個重要的研究領域,因為自然語言處理涉及理解人類語言的能力,如何讓電腦使用機器學習技術來處理及解讀文字和資料並學習人類理解語義與運用人類語言至各領域如電商、金融、法律、新聞輿情等。本課程將介紹自然語言處理的概念並從實作練習中建立學生應用自然語言處理的能力。

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

人工智慧倫理與人權|Course taught in Chinese

Instructed by
Shao-Man Lee
Alan Yi-Yu Hsu

可解釋的人工智慧,解析人工智慧自動決策背後的理由,是人工智慧技術進階研發、提升人類對人工智慧之信任的關鍵。如何於自動化決策中提供有意義的資訊給資料主體,除了有一般性的考量外,於不同應用場域,更有不同專業知識與價值的注入。本課程將概述相關 XAI 技術(例如,XAI 分類、事後和透明方法),並以法律或醫療應用作為主要討論脈絡。結合專題討論以及實作訓練,預計帶給同學實作技能與新興研究的視野。 本課程歸納並揭露機器學習模型產生結果之黑盒特性,並以醫療應用資料為輔,並讓學習者實作與解析自建構機器學習模型之可解釋性報告。

Listed in MS Degree in AI Robotics、MS Degree in Intelligent Computing、Intelligent Computing Industrial Doctorate Program、Intelligent Computing Credit Program

基於深度學習的巨量視覺資料分析|English

Instructed by
Wei-Ta Chu

本課程旨在培養學生了解深度學習在巨量視覺資料分析中的原理與應用,內容含括基礎的機器學習、類神經網路、卷積神經網路、transformers到最新深度學習模型應用到各種視覺分析課題。課程內容及作業形式源自美國華盛頓大學電機與電腦工程學系,可讓學生充分感受美國頂尖大學的課程節奏與強度。

Listed in MS Degree in AI Robotics、MS Degree in Intelligent Computing、Intelligent Computing Industrial Doctorate Program、Intelligent Computing Credit Program

資料探勘與社群網路分析|Course taught in Chinese

Instructed by

由於網路技術爆炸性的增長,資料以極快的速度生成和在世界上傳播。然而,我們很容易被大量的資料所淹沒但無法得到知識。資料探勘是從大量的資料中發現知識。它的目的是從大量的資料中提取有趣的,不是顯而易見的,隱含的,先前未知的,潛在有用的模式或知識。其中社群網路為近年來快速興起之資訊分享平台,我們需要新的方法分析大量的社群資料,並從中找尋有用的知識。

Listed in MS Degree in AI Robotics、MS Degree in Intelligent Computing、Intelligent Computing Industrial Doctorate Program、Intelligent Computing Credit Program

高等計算機結構與人工智慧晶片設計|Course taught in Chinese

Instructed by
Wei-Fen Lin

本課程對於高等計算機結構與AI晶片設計進行了進階主題探討。為了讓大家為學習AI晶片設計做好準備。課前會教紹一些進階的高等計算機結構主題,以及AI晶片設計上需要的一些基本知識,課程另外還包含9份實作Lab作業,以幫助學生學習相關實作技能。本課程推薦給想深入學習AI晶片設計的人。

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