Human-Centered Robotic and Automation Laboratory
人中心機器人與自動化實驗室
TEACHING
Digital Signal Processing
DELTA MOOCX Free Online Course
https://univ.deltamoocx.net/courses/course-v1:AT+AT_001_1101+2021_09_13/about
Optimization
In this course, unconstrained optimization problems, linear programming, and nonlinear constrained optimization problems are presented. Mathematical derivations and MATLAB simulations are used to analyze and obtain the solutions to these problems. For unconstrained optimization problems, one- dimensional search, gradient, Newton’s, conjugate direction, quasi-Newton methods are presented. For linear programming problems, simplex method is introduced. Last, Lagrange and Karush-Kuhn-Tucker multipliers are presented for solving nonlinear constrained optimization problems.
Robotics
This course consists of lectures on the theory of robotics. In the beginning of this course, kinematics, differential motions, and dynamics are reviewed. Also, trajectory planning and motion control. Last, actuators and sensors are mentioned.
https://univ.deltamoocx.net/courses/course-v1:AT+AT_010_1101+2021_09_13/about
智慧機器人學 Intelligent Robotics
ITRI College + Non-free course
https://collegeplus.itri.org.tw/courses/intelligence_robotics/
Machine Learning
Understand the application of Python programming language in machine learning, as well as the theoretical background of machine learning, assess how to use appropriate algorithms in practical examples, content includes regression analysis in data prediction, data dimensionality reduction methods in data feature extraction, data classification methods including support vectors Machine and neural network-like methods. The course mainly cooperates with Python examples to enhance students' understanding of machine learning related knowledge and enhance their competitiveness in the information processing era.
Neural Network
Introduction, layer perceptron, backpropagation network, learning vector quantization, self-organizing map, hopfield neural network, and applications.
Deep Learning
Introduction to AI, Python, Tensorflow, CNN, ResNet, RNN and LSTM, RCNN, Fast-CNN & Faster-CNN, Yolo, object detection competition, Autoencoder, VAE, and GAN .
