Repository for summary and code of [Machine Learning for Visual Understanding] course
This course was held at Seoul National University, Graduate School of Data Science. (Spring 2021)
This course covers mathematical modeling and machine learning techniques to analyze visual (and other multimedia) data. Specifically, this course focuses on fundamental machine learning and recent deep learning methods that are widely used in visual data analysis, and discusses how these methods are applied to solve various problems with visual data. This course consists of lectures, practices, and a team project. Topics include * Review of machine learning and neural networks, * Convolutional Neural network (CNNs), * Recurrent neural networks (RNNs) * Image problems (image classification, object detection, segmentation), * Video problems (video classification, action recognition, temporal localization, tracking), * Multi-modal data analysis (visual-audio-text), * Generative modeling, and more.