B.E Artificial Intelligence and Machine Learning Advanced AI Lab Syllabus - Mumbai University

Module 1: Design and implement a Hidden Markov Models for outcome prediction.

Module 2: Design and implement a Bayesian Network for outcome prediction.

Module 3: Design and implement a Gaussian Mixture Models for outcome prediction.

Module 4: Build and Train a Generative Multi-Layer Network Model using appropriate dataset.

Module 5: Build and Train a Deep Convolution Generative Multi-Layer (DCGAN) Network Model for an image based dataset.

Module 6: Develop a Conditional GAN (CGAN) Network to direct the image generation process of the generator model.

Module 7: Train a variational autoencoder using Tensorflow on Fashion MNIST

Module 8: Explore the working of any pre-trained model towards outcome generation.

Module 9: Implement and analyze the working of Local Interpretable Model-agnostic Explanations(LIME) supervised model.

Module 10: Case-study on the emerging technologies in AI like Metaverse, Augmented reality etc.

Module 11: Mini Project Report: For any one chosen real world application as per the syllabus of CSC801 : Advanced AI.

Module 12: Implementation and Presentation of Mini Project

Useful Links

• 1 https://nptel.ac.in/courses/106106224
• 2 https://www.tensorflow.org/tutorials/generative/cvae
• 3 https://www.analyticsvidhya.com/blog/2022/07/everything-you-need-to-know-about-lime/
• 4 https://onlinecourses.nptel.ac.in/noc20_cs62/preview
• 5 https://machinelearningmastery.com/what-are-generative-adversarial-networks-gans/