B.E. (Automation and Robotics) Deep Learning Syllabus - Mumbai University 2026

The University has moved this degree onto NEP 2020 one year at a time. The first and second years are NEP 2020 syllabi; the third and fourth years are still examined on the REV-2019 'C' Scheme, which is what the University sets for them this year.

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Syllabus for Deep Learning

Semester 8 · Fourth Year AR

Module 1: Fundamentals of Neural Network

Biological neuron, Mc-Culloch Pitts Neuron, Perceptron, Perceptron Learning, Delta learning, Multilayer Perceptron: Linearly separable, linearly non separable classes. Deep Networks: Fundamentals, Brief History, Three Classes of Deep Learning Basic Terminologies of Deep Learning

Module 2: Training, Optimization and Regularization of D e e p Neural Network Training Feedforward DNN

Multi Layered Feed Forward Neural Network, Learning Factors, Activation functions: Tanh, Logistic, Linear, Softmax, ReLU, Leaky ReLU, Loss functions: Squared Error loss, Cross Entropy, Choosing output function and loss function Optimization Learning with backpropagation, Learning Parameters: Gradient Descent (GD), Stochastic and Mini Batch GD, Momentum Based GD, Nesterov Accelerated GD, AdaGrad, Adam, RMSProp Regularization Overview of Overfitting, Types of biases, Bias Variance Tradeoff Regularization Methods: L1, L2 regularization, Parameter sharing, Dropout, Weight Decay, Batch normalization, Early stopping, Data Augmentation, adding noise to input and output

Module 3: Autoencoders: Unsupervised Learning

Introduction, Linear Autoencoder, Undercomplete Autoencoder, Overcomplete Autoencoders, Regularization in Autoencoders Denoising Autoencoders, Sparse Autoencoders, Contractive Autoencoders Application of Autoencoders: Image Compression

Module 4: Convolutional Neural Networks (CNN): Supervised Learning Convolution operation, Padding, Stride, Relation between input, output and filter size, CNN architecture: Convolution layer, Pooling Layer, Weight Sharing in CNN, Fully Connected NN vs CNN, Variants of basic Convolution function

Modern Deep Learning Architectures: LeNET: Architecture, AlexNET: Architecture

Module 5: Recurrent Neural Networks (RNN)

Sequence Learning Problem, Unfolding Computational graphs, Recurrent Neural Network, Bidirectional RNN, Backpropagation Through Time (BTT), Vanishing and Exploding Gradients, Truncated BTT Long Short Term Memory: Selective Read, Selective write, Selective Forget, Gated Recurrent Unit

Module 6: Recent Trends and Applications

Generative Adversarial Network (GAN): Architecture, Applications: Image Generation, DeepFake

Textbooks

1 Ian Goodfellow, Yoshua Bengio, Aaron Courville. “Deep Learning”, MIT Press Ltd, 2016
2 Li Deng and Dong Yu, “Deep Learning Methods and Applications”, Publishers Inc.
3 Satish Kumar "Neural Networks A Classroom Approach" Tata McGraw-Hill.
4 JM Zurada “Introduction to Artificial Neural Systems”, Jaico Publishing House
5 M. J. Kochenderfer, Tim A. Wheeler. “Algorithms for Optimization”, MIt Press. Buduma, N. and Locascio, N., “Fundamentals of deep learning: Designing next-generation 1 machine intelligence algorithms" 2017. O'Reilly Media, Inc.".
2 François Chollet. “Deep learning with Python “(Vol. 361). 2018 New York: Manning.
3 Douwe Osinga. “Deep Learning Cookbook”, O’REILLY, SPD Publishers, Delhi. Simon Haykin, Neural Network- A Comprehensive Foundation- Prentice Hall 4 International, Inc
5 S. N. Sivanandam and S. N. Deepa, Principles of soft computing-Wiley India
1 https://nptel.ac. https://deeplearning.cs.cmu.edu/S21/index.html
2 http://www.cse.iitm.ac.in/~miteshk/CS6910.html
3 https://nptel.ac.in/courses/106/106/106106184/
4 https://www.deeplearningbook.org/ U University of Mumbai

Reproduced from the University of Mumbai syllabus for B.E. (Automation and Robotics) under REV-2019 'C' Scheme, in force from the academic year 2024-25. Wording is as printed in that syllabus. Module numbering is as printed there too.

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.

PDF 7.40 (R-A) B.E. (Automation & Robotics) Sem I & II (Revised, NEP 2020) NEP 2020 syllabus Read full PDF Read

PDF 6.48 (N) B.E. (Automation and Robotics) Sem III & IV (NEP 2020) NEP 2020 syllabus Read full PDF Read

PDF 6.8 (N) B.E. (Automation and Robotics) Third Year, Sem V & VI (REV-2019 'C' Scheme) REV-2019 'C' Scheme syllabus Read full PDF Read

PDF B.E. (Automation and Robotics) Fourth Year, Sem VII & VIII (REV-2019 'C' Scheme) REV-2019 'C' Scheme syllabus Read full PDF Read

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