This course will study the theory and applications of many foundational machine learning methods. Several supervised, semi-supervised and unsupervised learning approaches will be explored, including Bayesian methods, decision trees, kernel-based methods and neural networks methods, as well as areas of clustering and dimension reduction. We will also discuss how to model problems as machine learning problems. Methods discussed will be applicable to natural language processing, speech recognition, computer vision, data mining, adaptive computer systems and other areas. Prerequisites:STAT 3060 or equivalent,STAT 3050 or equivalent ,MATH 2120 or equivalent, MATH 2111 or equivalent , Successful completion of atleast two university level computer programming courses Recommended Requisites: STAT 5310, STAT 5320 DASC 5410
This course will study the theory and applications of many foundational machine learning methods. Several supervised, semi-supervised and unsupervised learning approaches will be explored, including Bayesian methods, decision trees, kernel-based methods and neural networks methods, as well as areas of clustering and dimension reduction. We will also discuss how to model problems as machine learning problems. Methods discussed will be applicable to natural language processing, speech recognition, computer vision, data mining, adaptive computer systems and other areas. Prerequisites:STAT 3060 or equivalent,STAT 3050 or equivalent ,MATH 2120 or equivalent, MATH 2111 or equivalent , Successful completion of atleast two university level computer programming courses Recommended Requisites: STAT 5310, STAT 5320 DASC 5410
