COURSE

DSAA2011

Machine Learning

Information Hub / Data Science and Analytics · 3 Credits

Machine learning is an exciting and fast-growing field that leverages data to build models which can make predictions or decisions. This is an introductory machine learning course that covers fundamental topics in model assessment and selection, supervised learning (e.g., linear regression, logistic regression, neural networks, deep learning, support vector machines, Bayes classifiers, decision trees, ensemble methods); unsupervised learning (e.g., clustering, dimensionality reduction); and reinforcement learning. Students will also gain practical programming skills in machine learning to tackle real-world problems. Basic knowledge on mathematics (e.g., basic of probability theory, linear algebra, calculus and optimization), programming (e.g., Python / C++ / Matlab) and data science are essential and will benefit the study of this course.