Electronics Design Lab 〈AI for Robot Intelligence: Perception, Planning and Control〉

This course is an introduction to continuous-time and discrete-time signals and systems. The course covers Fourier series, Fourier transform, Laplace transform, and z-transform. Various types of systems with emphasis on linear time invariant system is studied.

This course covers data structures, algorithms, JAVA for electron electronics engineering. We study object-oriented programming techniques and use programming language C, JAVA.

In this course, we discuss such various topics in probability theory and introductory random processes as probability, random variables, expectations, characteristic functions, random vectors, random processes, correlation functions, and power spectrum. From time to time, homework problems will be assigned, usually not for mandatory submission.

This course will cover general methods for analysis and design of the dynamic system. The main contents include modeling in the frequency and time domain, time response, reduction of multiple subsystems, stability, steady-state error, root locus technique, frequency response technique, and design via frequency response and state-space.
(Prerequisite: EE202)

Introduces the principles, algorithms and application of machine learning from the point of modeling and prediction; learning problem representation.

This course will cover concepts such as representation, over-fitting, regularization, and generalization; topics such as clustering, classification, regression, recommendation problems, probabilistic modeling, reinforcement learning, and various on-line algorithms. It will also introduce a support vector machine and deep learning.