CMPS 410B Machine Learning
This course is designed to survey the significant features of machine learning, which is the science of getting computers to act without being explicitly programmed. The course covers the basic ideas and intuition behind modern machine learning methods and the most effective machine learning techniques including linear regression, logistic Regression, Regularization, Neural Networks, Support Vector Machines, clustering, kernel methods, Dimensionality Reduction, Anomaly Detection, Recommender Systems, reinforcement learning, and adaptive control. The student will complete numerous projects and homework to create the opportunity for an depth understanding of the field. The course provides sufficient knowledge of statistics, linear algebra, optimization, and computer science helping students to understand the key concepts of machine learning techniques. At the end of this course, the student should acquire sufficient knowledge of the field to be able to understand the machine learning problems and to be able to analyze large volumes of data at high speed to create automated systems.