4- Course Content :-
|
Topic |
No. of hours |
Lecture |
Tutorial/Practical |
|
Introduction. |
3 |
3 |
- |
|
Supervised Learning. |
3 |
3 |
- |
|
Bayesian Decision Theory. |
3 |
3 |
- |
|
Parametric Methods. |
3 |
3 |
- |
|
Multivariate Methods. |
3 |
3 |
- |
|
Dimensionality Reduction. |
3 |
3 |
- |
|
Clustering. |
3 |
3 |
- |
|
Nonparametric Methods. |
3 |
3 |
- |
|
Decision Trees. |
3 |
3 |
- |
|
Linear Discrimination. |
3 |
3 |
- |
|
Multilayer Perceptrons. |
3 |
3 |
- |
|
Local Models. |
3 |
3 |
- |
|
Kernel Machines. |
3 |
3 |
- |
|
Bayesian Estimation. |
3 |
3 |
- |