Course Outcomes:
After completion of this course, students will be able to –
3MDC2-13.1 |
Understand the basic concept of pattern recognition, probability distribution, regression and its types. |
3MDC2-13.2 |
Apply procedures for error minimization and linear programming algorithms for pattern recognition. |
3MDC2-13.3 |
Apply k-means, fuzzy, and hierarchical clustering methods for unsupervised learning |
3MDC2-13.4 |
Analyze back propagation, training methods and deep learning for neural networks |
3MDC2-13.5 |
Differentiate among types of classifiers for algorithm independent machine learning. |
CO-PO/PSO Mapping Matrix
COs |
PO 1 |
PO 2 |
PO 3 |
PSO 1 |
PSO 2 |
3MDC2-13.1 |
3 |
1 |
2 |
3 |
1 |
3MDC2-13.2 |
3 |
1 |
2 |
3 |
2 |
3MDC2-13.3 |
3 |
1 |
2 |
3 |
2 |
3MDC2-13.4 |
3 |
1 |
2 |
3 |
1 |
3MDC2-13.5 |
3 |
1 |
2 |
3 |
2 |
3MDC2-13 |
3 |
1 |
2 |
3 |
2 |