| Course Name | Pattern Recognition |
| Course Code | 15CSE361 |
| Program | B. Tech. in Computer Science and Engineering |
| Year Taught | 2019 |
Introduction: Machine perception – Pattern recognition systems – Design cycle – Learning and adaptation – Bayesian decision theory – minimum error rate classification – discriminant functions – decision surfaces – normal density based discriminant functions – Maximum likelihood estimation – Bayesian estimation.
Bayesian parameter estimation – Gaussian case – problems of dimensionality – Components analysis and discriminants – hidden Markov models, Non-parametric Techniques: density estimation – parzen windows – nearest neighbourhood estimation – linear discriminant functions and decision surfaces – two category linearly separable case – perception criterion function.
Non-Metric Methods: decision trees – CART methods – algorithm independent machine learning- bias and variance – regression and classification – classifiers – Unsupervised learning and clustering – mixture densities and identifiably – hierarchical clustering – low dimensional representation – multidimensional scaling.
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