Machine Learning and Deep Learning

Catalog description

This course examines the concepts, principles, and application of machine learning (ML) and deep learning (DL) by providing fundamental knowledge of ML/DL and hands-on experiences to develop ML/DL models in real-world scenarios. After taking this course, students will not only be able to apply the state-of-the-art ML/DL algorithms in their own projects but also be able to develop their own ML/DL algorithms. The topics of this course include supervised learning, weakly supervised learning, self-supervised learning, classification, regression, and detection. This course will introduce a wide range of ML/DL algorithms, such as Linear Regression, Logistic Regression, Deep Neural Networks (MLP), Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), Transformers, and Vision Transformers (ViT).