M.Sc. (Information Technology) Advanced Artificial Intelligence Theory Syllabus - Mumbai University 2026
This is the University of Mumbai syllabus for M.Sc. (Information Technology) under NEP 2020, in force from the academic year 2024-25. Semesters I and II are set by item 6.29 (N) and Semesters III and IV by item 6.2 (N). The University examines this programme under form 1113161, whose Summer 2026 timetable is headed NEP-2020; the distance and older Choice Based modes of the same degree are examined under different forms and different papers.
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Syllabus for Advanced Artificial Intelligence Theory
- Introduction to Artificial Intelligence
- Intelligent Agents
- Problem-solving using search algorithms
- Knowledge Representation [Ref:01] Deep Learning" by Ian Goodfellow, Yoshua Bengio, and Aaron Courville
- Introduction to advanced AI concepts
- Deep learning and neural networks
- Natural language processing and understanding
- Reinforcement learning
- AI applications in various domains such as healthcare, finance, and robotics [Ref:02] Artificial Intelligence: A Modern Approach" by Stuart Russell and Peter Norvig
- Machine Learning Paradigms: Machine Learning systems,
- supervised and un-supervised learning, inductive learning,
- deductive learning, clustering, support vector machines, cased
- based reasoning and learning.
- Artificial Neural Networks: Artificial Neural Networks,
- Single-Layer feedforward networks, multi-layer feed-forward
- networks, radial basis function networks, design issues of
- artificial neural networks and recurrent networks [Ref:05] Artificial Intelligence”, Saroj Kaushik, Cengage, 1st Edition, 2019
- Introduction to generative AI and its applications
- Generative adversarial networks (GANs)
- Variational autoencoders
- Text generation and image synthesis using generative AI
- Ethical considerations in generative AI [Ref:04] Python Machine Learning" by Sebastian Raschka and Vahid Mirjalili 1. 1.Understand the fundamental principles and concepts of Artificial Intelligence. 2. Implement intelligent agents for different applications. 3. Understand advanced AI concepts and techniques 4. Demonstrate proficiency in deep learning and neural networks 5. Understand the concepts and applications of generative AI 6. 6.Implement generative adversarial networks and variational autoencoders 7. 7.Develop skills in using neural networks for image recognition and text generation 8. 8.Create and train GAN models for image synthesis
Books and References
- 1 Deep Learning Ian Goodfellow, Yoshua MIT Pres 2020 Bengio, and Aaron Courville
- 2 Artificial Intelligence: A Modern Stuart Russell and Peter Prentice 2020 Approach Norvig Hall
- 3 Reinforcement Learning: An Richard S. Sutton and MIT Press 2023 Introduction Andrew G. Barto
- 4 Python Machine Learning Sebastian Raschka and Packt 2021 Vahid Mirjalili Publication
- 5 Cngage Artificial Intelligence Saroj Kaushik, Cengage 2019 Publication
Reproduced from the University of Mumbai syllabus for M.Sc. (Information Technology) under NEP 2020, in force from the academic year 2024-25. Wording, unit numbering and lecture allocation are as printed in that syllabus. The PDF above is the syllabus's own page, unaltered.
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