JAWAHARLAL NEHRU TECHNOLOGICAL UNIVERSITY HYDERABAD

Course Objectives:

To provide exposure of working on Deep learning. To design and implement solutions for real life problems

Course Outcomes:

Simulate/implement given problem scenario and analyze its performance. Design solutions for real life problems.

List of Programs: Build an Artificial Neural Network by implementing the Backpropagation algorithm and test the same using appropriate data sets. Write a program to implement the naïve Bayesian classifier for a sample training data set stored as a .CSV file. Compute the accuracy of the classifier, considering few test data sets. Write a program to construct a skip gram model using NLP Write a program to construct a Bayesian network considering medical data. Use this model to demonstrate the diagnosis of heart patients using standard Heart Disease Data Set. You can use Python ML library classes/API. Write a program to implement Continuous Bag of Words Model using KNN Algorithm (using Python). Build a model that takes an image as input and determines whether the image contains a picture of a dog or a cat. Use CIFAR-10 dataset and build an image classification model that will be able to identify what class the input image belongs to. Implement a Human Face Recognition Model and determine the accuracy in detecting the bounding boxes of the human face. Build an image classification model to detect if the person has cancer or not. Build a chatbot to identify the context the user is asking and then provide it with the relevant answer.

Text Books:

Deep Learning by Ian Goodfellow, Yoshua Bengio and Aaron Courville, MIT Press. The Elements of Statistical Learning by T. Hastie, R. Tibshirani, and J. Friedman, Springer. Probabilistic Graphical Models by D. Koller, and N. Friedman, MIT Press.

M.Tech. (Data Science) I Year II Semester