Pattern Recognition: From Theory to Intelligent Application

This course provides a comprehensive foundation in the core principles and modern practices of pattern recognition. Learn to develop systems that automatically discover patterns in data, classify objects, and make intelligent decisions. Topics include Bayesian decision theory, supervised learning (SVM, k-NN), unsupervised learning (clustering, PCA), and an introduction to deep learning for complex pattern recognition. Gain hands-on experience by implementing algorithms in Python to solve real-world problems in computer vision, speech processing, and beyond.

Keywords for search: Pattern Recognition, Machine Learning, Classification, Clustering, Bayesian, SVM, k-NN, PCA, Deep Learning, Python, Computer Vision, Data Analysis.