微控制器
原理與應用
感測器
原理與應用
機器人專題
Matlab
的工程應用
機器學習
應用概論
課程概述
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微控制器原理與應用-機電整合(一)
本課程簡介微控制器,並啟發大一新生對於機電整合系統之興趣。
課程內容分成兩部分:(1)Arduino模組與,(2)AVR ATmega328P微控制器。課程內容包含了微控制器架構、指令集、組合語言、輸出輸入、中斷、傳輸、計時器等。本課程包含許多實習與一個期末專題,幫助同學熟悉這些技術的應用。
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感測器原理與應用-機電整合(二)
本課程旨在讓學生瞭解感測(量測)的原理與應用。
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Ability to analyze and design sensor control systems.
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Ability to implement bio-mechatronic systems.
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Ability of critical thinking and problem solving.
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Ability of communication, teamwork and dedication.
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機器人專題
此課程與喬治亞大學(University of Georgia)的 Dr. Chi Thai 跨國連合授課。
The goal of this course is to provide students with an advanced practicum in Embedded Robotics wherein the students will learn about the programming of embedded controllers, the interfacing of sensors, the actuation of servo motors, inter-computer serial communications (RS-232 and ZigBee), and the control of autonomous as well as remotely piloted systems. The student will be programming using a high-level integrated environment called RoboPlus, and also will be practicing lower-level programming using the C/C++ language. These concepts and methodologies will be demonstrated in class with sample codes and the students can expand on these ideas further with a series of robotic projects (of increasing complexity) throughout the semester such as car robots, simple bipedal robots (7-8 degrees of freedom) and humanoid robots (18 degrees of freedom). Possible projects can be about master-slave robots, search and rescue robot teams, mobile wireless sensor networks, humanoid robot balance control, intruder (motion) detection, image recognition and object tracking, and humanoid robot negotiating stairs with varying tread depths. Upon completion of this course, students should have an integrated hardware/software understanding of embedded robotic systems whether they would be autonomous or remotely piloted.
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MATLAB之工程應用
本課程旨在讓學生瞭解MATLAB的使用方法,使其成為一個良好的研究工具。以MATLAB解決工程問題,並從中熟悉MATLAB之初、中階使用與程式撰寫。
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機器學習應用概論
本課程是一門概論課程,旨在介紹主要機器學習演算法,提供各種方法的概觀。課程內容涵蓋機器學習基本概念與其演算法之使用。本課程將會包含一個專題實作,期望將所學之理論加以應用。課程主題包括:
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Linear regression
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PCA and PCR
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Overfitting
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Support vector machine
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Decision tree
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Artificial neural network
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K-means
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KNN
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Linear discriminant analysis and general discriminant analysis