Prof. Catherine Qi Zhao
Overview: Machine learning is one of the fastest growing fields in computer science. The objective of this class is to provide rigorous training in conceptual, theoretical and experimental machine learning through lectures and hands-on experience. Topics include various supervised and unsupervised learning methods including a basic introduction to deep neural networks. The course will not only teach the theoretical underpinnings of machine learning, but also train the practical know-how to powerfully apply these methods to various problems and applications pertaining to machine learning and artificial intelligence.
Prof. Catherine Qi Zhao
Overview: The emergence of large visual data, machine learning algorithms, and advancement in hardware has enabled significant breakthrough in vision and related applications. This class will discuss recent progress and findings in learning based approaches to computer vision, one of the most fast growing and exciting fields in artificial intelligence. Topics include a review of popular machine learning methods and cutting edge knowledge on high-level visual tasks for various application domains. Analogue with the biological visual system will also be introduced and discussed.
Prof. Catherine Qi Zhao
Overview: Linear algebra plays an important role in areas of computer science such as machine learning, robotics, and computer graphics. This course is an introduction to linear algebra and to matrix theory and computation for computer scientists. It covers fundamental of linear algebra (vectors, matrices, determinants, eigenvalues, etc.), standard algorithms for solving matrix problems (solving linear systems, finding least squares approximants, computing singular value decompositions, etc.), and certain applications of linear algebra in computer science and related areas. The class will blend theory, computation, and applications.