IS460

Undergraduate

Machine learning is one of the fundamental subjects in the field of artificial intelligence. It focuses on computer programs that automatically improve their performance through experience—for example, by learning from data to recognize images or speech, classify text documents, detect credit card fraud, drive autonomous vehicles, identify fake news and harmful videos, recognize emotions, predict stock market trends, and perform natural language processing tasks. This course covers fundamental theory and practical algorithms, together with their applications, from a variety of perspectives. It includes supervised learning topics such as the Naïve Bayes classifier, linear and logistic regression, neural networks, and deep learning, as well as unsupervised learning topics including exploratory data analysis, dimensionality reduction, principal component analysis (PCA), and singular value decomposition (SVD).The course covers both traditional (shallow) learning methods, such as Support Vector Machines (SVMs), and state-of-the-art deep learning approaches, including Recurrent Neural Networks (RNNs), Convolutional Neural Networks (CNNs), and applications involving Large Language Models (LLMs). These advanced topics are not assessed in exam, not due to a lack of importance, but to avoid excessive course load. The course is intended to prepare students with a basic understanding of machine learning fundamentals and to equip them with the capability to apply machine learning techniques to real-world applications. One programming-related course and one statistics- or math-related course are prerequisites.

Data Analytics, Algorithm Analysis, Emerging Technology Synthesis, Problem-solving & analysis, Applications Development

Disciplinary Knowledge, Multidisciplinary Knowledge, Interdisciplinary Knowledge, Critical thinking & problem solving, Innovation and enterprising skills, Collaboration and leadership, Communication, Intercultural understanding and sensitivity, Understanding of global and Asian perspectives, Ethics and social responsibility, Understanding of sustainability issues, Self-directed learning