Course Details

 

Course Details Language of Instruction English Level of Course Unit Doctorate's Degree Department / Program ELECTRICAL AND COMPUTER ENGINEERING Type of Program Formal Education Type of Course Unit Elective Course Delivery Method Face To Face Objectives of the Course Gain an understanding of pattern recognition methods Learn the techniques used for developing pattern recognition models Develop skills for practical aspects of deep learning Apply the concepts learned to a real problem Course Content This course provides an introduction to pattern recognition. It covers Bayesian and frequentist statistics, Bayesian learning methods, decision theory, generalized linear models and the exponential family, and regression models. Mathematical principles will be explained to provide a solid foundation for pattern recognition. Methods will be implemented by a software and applied on various pattern recognition problems. Course Methods and Techniques This course covers basic methods used in pattern recognition, such as Bayesian learning, generalized linear models, and decision theory. Statistical approaches such as exponential families and regression models are used in the classification and modeling of patterns. Students gain practical skills by testing these techniques on practical examples through programming. Prerequisites and co-requisities None Course Coordinator None Name of Lecturers Associate Prof.Dr. Zafer Aydın Avesis zafer.aydin@agu.edu.tr Assistants None Work Placement(s) No Recommended or Required Reading Resources 1. Machine Learning, a Probabilistic Perspective, K. P. Murphy, MIT Press, 2012. 2. Pattern Recognition and Machine Learning, C. Bishop, Springer, 2006. 3. Elements of Statistical Learning: Data Mining, Inference and Prediction, T. Hastive, R. Tibshirani, Springer, 2016. Course Notes 1. Machine Learning, a Probabilistic Perspective, K. P. Murphy, MIT Press, 2012. 2. Pattern Recognition and Machine Learning, C. Bishop, Springer, 2006. 3. Elements of Statistical Learning: Data Mining, Inference and Prediction, T. Hastive, R. Tibshirani, Springer, 2016. Documents Will be shared on Canvas Assignments Will be shared on Canvas Exams Will be shared on Canvas Course Category Mathematics and Basic Sciences %20 Engineering %20 Engineering Design %20 Social Sciences %0 Education %0 Science %20 Health %0 Field %20

 
 
 
 
  
  https://sis.agu.edu.tr/oibs/bologna/progCourseDetails.aspx?curCourse=77912&lang=en