Executive Development Programme in Pattern Recognition Methods

• Introduction to Pattern Recognition Methods: Fundamentals, concepts, and applications
• Statistical Pattern Recognition: Probability theory, Bayesian decision theory, and parameter estimation
• Feature Extraction and Selection: Dimensionality reduction, feature engineering, and transformations
• Classification Techniques: Supervised learning, k-Nearest Neighbors, and Support Vector Machines
• Unsupervised Learning and Clustering: Hierarchical and non-hierarchical clustering, K-means, and DBSCAN
• Neural Networks and Deep Learning: Artificial Neural Networks, Convolutional Neural Networks, and Recurrent Neural Networks
• Computer Vision and Image Processing: Object detection, feature descriptors, and image enhancement
• Natural Language Processing: Text processing, topic modeling, and sentiment analysis
• Evaluation Metrics and Model Selection: Performance assessment, overfitting, and cross-validation