Course Details

Subject {L-T-P / C} : EC6674 : Machine Intelligence Laboratory { 0-0-3 / 2}

Subject Nature : Practical

Coordinator : Prof. Samit Ari

Syllabus

1. Development of k- nearest neighbors algorithm for classification of image data.
2. Implementation of k-means clustering algorithm for binary and multi-class classification of image data.
3. Development of expectation maximization (EM) algorithm for binary classification of the data and find the probabilities, means and variances of the respective classes.
4. Implement principle component analysis (PCA) technique on 2-D data and determine the Eigen vectors. Plot PCA space of the first two PCs.
5. Implement linear discriminant analysis (LDA) technique for data classification.
6. Apply PCA and LDA techniques for dimensionality reduction of feature vector.
7. Study of the SVM technique using MATLAB/C and apply this technique for data classification.
8. Study of the different deep learning techniques using MATLAB/C.
9. Design a feature map of a given data using convolution and pooling operation of convolutional neural network (CNN).
10. Minor project.

Course Objectives

Course Outcomes

A student will develop different machine intelligence algorithms e.g. EM algorithm, SVM, Deep learning using MATLAB/C platform for real time application.

Essential Reading

Supplementary Reading