Department: BSc Computer Science
Module Description: Pattern recognition theory and practice is concerned with the design, analysis, and development of methods for the classification or description of patterns, objects, signals, and processes. At the heart of this discipline is our ability infer the statistical behaviour of data from limited data sets, and to assign data to classes based on generalized notions of distances in a probabilistic space. Many commercial applications of pattern recognition exist today, including face detection and recognition, handwriting recognition, and speech recognition. Today, machine learning is one of the most active areas of Artificial Intelligence and is enjoying unprecedented levels of success. This course is designed to give the student a strong background in fundamentals of Pattern Recognition and machine learning using Deep Neural Network. Also introduce the student to the tools necessary to implement the deep learning algorithms.
Braga-Neto Ulisses de Mendonc̀§a (2020). Fundamentals of pattern recognition and machine learning Cham, Switzerland: Springer. Purchase eBook
Elgendy, M. (2020). Deep learning for vision systems. Simon and Schuster.
Michael Nielso, M. (2019). Neural networks and deep learning. Open resource
Christopher M. Bishop, C. M. (2023). Pattern Recognition and Machine Learning. Springer.
