Engineering - Course - Pattern Recognition

Review of Probability Theory, Conditional Probability and Bayes Rule, Random Vectors, Correlation, Covariance. Review of Linear Algebra, Linear Transformations. Decision Theory, ROC Curves, Likelihood Ratio Test, Linear and Quadratic Discriminants. Template-based Recognition, Feature Extraction, Eigenvector and Multilinear Analysis. Training Methods, Maximum Likelihood and Bayesian Parameter Estimation. Linear Discriminant/ Perceptron Learning, Optimization by Gradient Descent. Support Vector Machines. K-Nearest-Neighbor Classification. Non-parametric Classification, Density Estimation, Parzen Estimation. Unsupervised Learning, Clustering, Vector Quantization, K-means. Hidden Markov Models. Linear Dynamical Systems, Kalman Filtering. Bayesian Networks. Decision Trees.