---
◆濱野正浩老師-數學與演算法｜英文授課(Course taught in English)
Prof. Masahiro HAMANO-Introduction to Theory of Computation
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。越靈活應用演算法，越能加速執行任務的效率。然而演算法越學越多，要如何選用最有效率的演算法呢？此時我們便需要了解「複雜度理論」。
這堂課屬於入門級別，自數學角度切入，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。
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.
#提升運算效率從學會判斷選擇哪個演算法開始
#認識電腦的計算原理與量子電腦的革新
#Introductory level course
#Elevating the computing efficiency starting from choosing the best algorithm
#Learning the theory of computation and the innovation of quantum computation
---
◆徐禕佑老師-AI圖像識別及機器人實驗室導論｜英文授課(Course taught in English)
Prof. Allen Hsu-Introduction to Image Recognition AI and Robotics lab
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 trip(in-person) visit to NITKC under safe conditions.
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.
#數學工具在手，加速建模效率
#機器人學的預修課程
#追求精簡優美的演算法從掌握更多模型工具開始
#accelerate modelling with math
#essential to robotics
#better modelling skills in hand, more elegant your algorithms would be