XAI502 – Probability and Statistics
This course provides a probability and statistics theory. Problems on events, independence, random variables, distribution and density functions, expectations, and characteristic functions. The lecture includes probability axioms, conditional probability, Bayes’ theorem, independence, discrete and continuous random variables, multiple random variables, the sum of random variables, the sample mean, and introduction to statistical inference and test of hypothesis.
XAI602 – Computer Vision Application and Practice
This course deals with how a real live application can be endowed with visual perception. The issues discussed include motion tracking, image representation, edge detection, pixel-wise segmentation, human pose estimation, stereo vision, image classification, object recognition, optical flows, visual tracking, color vision, and advanced concepts in computer vision. Students are expected to implement vision algorithms through the term projects.
XAI501 – Machine Learning
This course covers basic machine learning concepts, including supervised learning and unsupervised learning, overfitting, regularization, and optimization. Also, theories and applications on the definition and methods of various machine learning problems such as dimension reduction, clustering, and anomaly detection will be discussed.
XAI511 – Neural Networks
This course covers concepts and applications of neural network models. Students can learn about the principles of neural network structures based on the mechanisms of biological neural cells.