CSCI 8590: Fundamentals of Deep Learning. Syllabus

This course is an introduction to deep learning, a branch of machine learning concerned with the development and application of neural networks. Deep learning trains the machine to learn patterns that it is presented with rather than requiring the human operator to define the patterns that the machine should look for. Deep learning is behind many recent advances in artificial intelligence, such as face recognition, speech recognition and autonomous driving. This course will cover the fundamentals of deep learning, learning theory, basic/advanced neural networks, and problem domains of many selected applications.

CSCI 8110: Advanced Topics in Artificial Intelligence (Advanced Machine/Deep Learning Applications). Syllabus

Machine learning is the scientific study of models that computers use to perform a specific task without explicit instructions. This graduate-level seminar-based course discusses machine(deep) learning applications that are essential in recent advances. Contents of this course will include various applications such as Autoencoders, Self-supervised Learning, Explainable Machine Learning, Object Detection, Knowledge Transfer, Generative Machine Learning Models, Robustness and Compression. More selected topics such as the Meta Learning and Image Semantic Segmentation can also be discussed based on the time availability. 

CSCI 3470: Machine Learning. Syllabus

This course discusses the fundamentals and algorithms of machine learning. Machine learning, as a subset of artificial intelligence, is the scientific study of models that computer systems use to perform a specific task without explicit instructions. Topics in this course will include discussions of: (i) supervised learning methods such as Classification, Support Vector Machine, and Regression; (ii) unsupervised learning methods such as Clustering, Dimensionality  Reduction, and Autoencoders; and (iii) more methods such as Self-supervised Learning and Semi- supervised Learning. Learning theories such as bias/variance trade-offs, Generalization and  Under-/Over-fitting could be discussed, as necessary.

CSCI 4450: Introduction to Artificial Intelligence. Syllabus

This course, Introduction to Artificial Intelligence, offers a comprehensive overview of the fundamental concepts, techniques, and applications of AI. Students will explore the evolution of AI from its early beginnings to modern advancements, study the foundations essential for understanding AI, and examine how AI systems represent knowledge and make decisions. Through lectures covering machine learning, deep learning, natural language processing, and computer vision, participants will gain insights into both theoretical underpinnings and practical applications across various sectors.