B.E. (Artificial Intelligence and Data Science) Advanced Artificial Intelligence Syllabus - Mumbai University

This is the Fourth Year BE AI and DS syllabus under CBCS REV-2019 'C' Scheme, in force from the academic year 2023-24. The University has published no NEP 2020 syllabus for Semesters V to VIII of any engineering branch, so this is the scheme you are examined on — exam form 1T01817 and 1T01818. The first and second years of the degree are on NEP 2020.

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Syllabus for Advanced Artificial Intelligence

Semester 8 · Fourth Year BE AI and DS · 3 credits · CBCS REV-2019 'C' Scheme

Module 1.0 08 hours

1.0 Generative and Probabilistic Models
1.1 1.2 Introduction: Overview of generative models and their importance in AI, Fundamentals of Probability theory and generative modeling, Introduction to GANs, VAEs and other generative models. Significance of generative models, Challenges with generative models. Probabilistic Models: Gaussian Mixture Models (GMMs), Hidden Markov Models (HMMs), Bayesian Networks, Markov Random Field (MRFs), Probabilistic Graphical Model.
2.0 Generative Adversarial Network
2.1 Basics of GAN : Generative Adversarial Networks (GANs) architecture, The discriminator model and generator model, Architecture and Training GANs, Vanilla GAN Architecture. GAN variants and improvements (DCGAN, WGAN, Conditional GAN, CycleGAN), Challenges- Training instability and model collapse, GAN applications in image synthesis and style transfer.
3.0 Variational Autoencoders
3.1 3.2 Introduction: Basic components of Variational Autoencoders(VAEs), Architecture and training of VAEs the loss function, Latent space representation and inference, Applications of VAEs in image generation. Types of Autoencoders: Undercomplete autoencoders, Sparse autoencoders, Contractive autoencoders, Denoising autoencoders, Variational Autoencoders (for generative modelling)
4.0 Transfer Learning
4.1 Introduction to transfer learning Basic terminologies, Pre-trained model and data sets, Feature extraction and fine tune transfer learning , Recent advancement in transfer learning : self-supervised learning and meta learning.
5.0 Ensemble learning
5.1 Ensemble Classifiers: Introduction to Ensemble Methods. Bagging and random forests, Boosting algorithms : AdaBoost Stacking and blending models, Extreme Gradient Boosting (XGBoost): XGBoost Regression and classification.
6.0 Nascent Technologies in AI
6.1 Convergence of AI with Augmented / Virtual reality techniques for product and process development Limitations of 2D Learning Environments, Evolution of virtual worlds and immersive technologies, Definition and concepts of Augmented Reality, Definition and concept of the Metaverse, Characteristics and components of the Metaverse, Challenges andopportunities in the Metaverse ecosystem, AI in the realm of emerging quantum computing paragms

Text Books

1 Foster, D., 2022. Generative deep learning. " O'Reilly Media, Inc.".
2 Koller, D. and Friedman, N., 2009. Probabilistic graphical models: principles and techniques. MIT press
3 Goodfellow, I., 2016. Deep Learning-Ian Goodfellow, Yoshua Bengio, Aaron Courville- Google Books
4 Murphy, K.P., 2012. Machine learning: a probabilistic perspective. MIT press
5 Zhou, Z.H., 2012. Ensemble methods: foundations and algorithms. CRC press.

References

1 Xiong, J., Hsiang, E.L., He, Z., Zhan, T. and Wu, S.T., 2021. Augmented reality and virtual reality displays: emerging technologies and future perspectives. Light: Science & Applications, 10(1), p.216.
2 Mystakidis, S., 2022. Metaverse. Encyclopedia, 2(1), pp.486-497
3 Gill, S.S., Xu, M., Ottaviani, C., Patros, P., Bahsoon, R., Shaghaghi, A., Golec, M., Stankovski, V., Wu, H., Abraham, A. and Singh, M., 2022. AI for next generation computing: Emerging trends and future directions. Internet of Things, 19, p.100514
4 Mangini, S., Tacchino, F., Gerace, D., Bajoni, D. and Macchiavello, C., 2021. Quantum computing models for artificial neural networks. Europhysics Letters, 134(1), p.10002.

Digital Material

https://nptel.ac.in/courses/106106201
https://onlinecourses.nptel.ac.in/noc20_cs62/preview
https://machinelearningmastery.com/what-are-generative-adversarial-networks-gans/

Reproduced from the University of Mumbai syllabus for B.E. (Artificial Intelligence and Data Science), item 6.12 (N), under CBCS REV-2019 'C' Scheme, in force from the academic year 2023-24. Wording, module numbering and hours are as printed in that syllabus.

The complete syllabus

This subject is cut from the University circular for its year. Open a document here if you want the whole thing rather than a single subject.

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