隨著資訊教育越來越普及，程式語言儼然成為這個世代的顯學。在各個程式語言中，又屬Python 最易入門，並且應用廣泛，例如：爬蟲、數據分析、影像處理、機器人控制與深度學習等。這堂課將教導Python的基礎語法與進階應用，培養學生使用Python實做的能力，讓學生可以利用Python解決生活、研究或工作上遇到的問題。

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在現今社會，無論你身處哪個領域，擁有跨人工智慧領域的知識是你脫穎而出的關鍵之一。此門課程希望將人工智慧帶給成大九大學院的同學，透過介紹人工智慧運算的基本概念與跨領域應用現狀，讓各學院的同學能系統性地進入人工智慧的世界，並經由實作過程融會貫通。

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AI技術逐漸應用於社會生活之中，在不久的將來極有可能與我們的生活息息相關。因此，AI技術應用的法律與管制政策成為我們重要的課題。 這堂課目的在於探討人工智慧治理的管制框架與主要議題，提供跨領域研究者所需要初步法律素養，希望能作為跨域思考的起點。課程除了講授概念外，更在於認識相關爭議：課堂著重同學藉由分組討論相互挑戰並完善個人想法。希望藉由注入民主社會多元價值於人工智慧研發應用的方式，幫助形塑整全的人工智慧治理願景。

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熱門領域的必備先修技能！無論是機器學習Machine Learning或是巨量資料分析，都需學生擁有機率與資料導論的知識能力。本課程將介紹基礎機率模型及統計方法，包括描述性統計，機率，隨機變數，及樣本平均值的分佈…等。本課亦會講授進階的統計方法：變異數分析和迴歸分析，學生將學習如何把統計方法應用於解決實際的問題。

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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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計算認知科學是一門運用計算模型理解腦神經元活動和行為的學科，結合認知科學、計算神經科學與人工智慧三領域。這堂課程希望讓學生了解神經網路的理論基礎與模型，以及大腦與認知科學研究議題與研究方法，進而了解計算模型的建構及其在認知科學研究的應用。對於非本科生而言，這堂課可以帶領你進入認知科學，了解心智運作的基礎理論與模型。目前計算認知科學已為人工智慧領域帶來突破，對於欲精進人工智慧的同學而言，是具啟發性的跨領域嘗試。

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