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.

To design and development of Machine intelligence algorithm for classification/ recognition of the data.

To develop and model the real time software for classification/ recognition of the data.

CO1: To understand and implement the basic clustering algorithm.
CO2: To implement the statistical approach for data classification.
CO3: To understand and implement the different dimensional reduction techniques
CO4: To understand and implement classification techniques like support vector machine (SVM) and deep learning techniques for data classification.
CO5: To be able to apply for different real-life applications.

C. M. Bishop, Pattern Recognition and Machine Learning, Springer

S. Haykin, Neural Networks - A Comprehensive Foundation, Peasrson Education, India

Tom M. Mitchell, Machine Learning, McGraw Hill Education (India)

Ethem Alpaydin, Introduction to Machine Learning, MIT Press, 2nd edition