| Course Name | Pattern Recognition |
| Course Code | 19CCE337 |
| Program | B. Tech. in Computer and Communication Engineering |
| Year Taught | 2019 |
Introduction – Applications of pattern recognition -Probability distribution basics – Discrete distributions and Continuous distributions – Conditional probability distribution and Joint probability distribution – Statistical decision Making – Introduction – Bayes’ theorem – conditionally independent features – Naïve bayes classifier – Decision Boundaries – Unequal costs of error – Estimation of error rates.
Nonparametric decision making – Introduction – histograms – K nearest neighbor method – adaptive decision Boundaries – adaptive discriminant functions – minimum squared error discriminant functions – Artificial neural Networks – Logistic regression – Perceptron – Multilayer feed forward neural network – Gradient descent method – back propagation -Dimensionality Reduction Techniques – Principal component analysis – Fisher discriminant analysis.
Evaluation Pattern
| Assessment | Internal | External |
| Periodical 1 (P1) | 15 | – |
| Periodical 2 (P2) | 15 | – |
| *Continuous Assessment (CA) | 20 | – |
| End Semester | – | 50 |
| *CA – Can be Quizzes, Assignment, Projects, and Reports. | ||
Objectives
Course Outcomes
CO – PO Mapping
| PO/PSO/CO | PO1 | PO2 | PO3 | PO4 | PO5 | PO6 | PO7 | PO8 | PO9 | PO10 | PO11 | PO12 | PSO1 | PSO2 |
| CO1 | 3 | 3 | 3 | 3 | – | – | – | – | – | 3 | – | 3 | 3 | 3 |
| CO2 | 3 | 3 | 3 | 3 | 3 | – | – | – | – | 3 | 2 | 3 | 3 | 3 |
| CO3 | 3 | 3 | 3 | 3 | 3 | – | – | – | – | 3 | 2 | 3 | 3 | 3 |
| CO4 | – | – | – | 3 | 3 | – | – | – | 2 | 3 | 2 | 3 | 3 | 3 |
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