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DEEP LEARNING AND REINFORCEMENT LEARNING Semester VII
Course Code: BAI701
CIE Marks: 50
Teaching Hours/Week (L:T:P: S): 3:0:2:0
SEE Marks: 50
Total Hours of Pedagogy: 40 hours Theory + 8-10 Lab slots
Total Marks: 100
Credits: 04
Exam Hours: Not Specified
Examination type (SEE): Theory/practical/Viva-Voce /Term-work/Others

Introduction to Deep Learning: Introduction, Shallow Learning, Deep Learning, Why to use Deep Learning, How Deep Learning Works, Deep Learning Challenges, How Learning Differs from Pure Optimization, Challenges in Neural Network Optimization.

Textbook 1: Ch 1.1 – 1.6, Textbook 2: 8.1,8.2

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Basics of Supervised Deep Learning: Introduction, Convolution Neural Network, Evolution of Convolution Neural Network, Architecture of CNN, Convolution Operation

Textbook 1: Ch 2.1 – 2.5

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Training Supervised Deep Learning Networks: Training Convolution Neural Networks, Gradient Descent-Based Optimization Techniques, Challenges in Training Deep Networks.

Supervised Deep Learning Architectures: LetNet-5, AlexNet

Text Book - 1 : Ch 3.2,3.4,3.5, Ch 4.2,4.3

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Recurrent and Recursive Neural Networks: Unfolding Computational Graphs, Recurrent Neural Network, Bidirectional RNNs, Deep Recurrent Networks, Recursive Neural Networks, The Long Short-Term Memory. Gated RNNs.

Text Book – 2: 10.1-10.3, 10.5, 10.6, 10.10

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Deep Reinforcement Learning: Introduction, Stateless Algorithms: Multi-Armed Bandits, The Basic Framework of Reinforcement Learning, case studies.

Textbook – 3: Chapter 9: 9.1,9.2,9.3, 9.7

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2022 SCHEME QUESTION PAPER

Model Set 1 Paper

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Model Set 2 Paper

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Model Set 2 Paper Solution

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Regular Paper

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Back Paper

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