Supervised and Unsupervised Learning Lab

Instructions:

1. The course objectives and course outcomes are identical to that of (Supervised and Unsupervised Learning) as this is the practical component of the corresponding theory paper.
2. The practical list shall be notified by the teacher in the first week of the class commencement under intimation to the office of the Head of Department / Institution in which the paper is being offered from the list of practicals below. Atleast 10 experiments must be performed by the students, they may be asked to do more. 

List of Programs

1. Introduction to JUPYTER IDE and its libraries Pandas and NumPy
2. Program to demonstrate Multiple Linear Regression
3. Program to demonstrate SVM based classification
4. Implement Boolean gates using perceptron
5. Program to demonstrate Back-Propagation Algorithm
6. Program to demonstrate k-means clustering algorithm
7. Program to demonstrate Agglomerative Hierarchical clustering
8. Program to demonstrate PCA on face recognition
9. Compare the performance of PCA and Autoencoders on a given dataset
10. Build Generative adversarial model for fake (news/image/audio/video) prediction.