Pattern Recognition

What you'll learn

• Statistical Decision Theory: Apply Bayesian decision theory, maximum likelihood estimation, and discriminant functions for optimal classification.
• Feature Engineering: Transform high-dimensional or raw data into compact, highly informative feature representations.
• Classifier Implementation: Design and evaluate parametric classifiers (Gaussian models) and non-parametric classifiers (k-Nearest Neighbors, Support Vector Machines).
• Sequence & Temporal Modeling: Analyze sequential and time-series data using Hidden Markov Models (HMMs) and dynamic clustering techniques.
• Deep Learning Integration: Leverage modern neural network architectures to solve complex pattern recognition challenges in computer vision and signal processing.