BA4028 Syllabus - Deep Learning And Artificial Intelligence - 2021 Regulation Anna University

BA4028 Syllabus - Deep Learning And Artificial Intelligence - 2021 Regulation Anna University

BA4028

DEEP LEARNING AND ARTIFICIAL INTELLIGENCE

 L T P C

3 0 0 3

COURSE OBJECTIVES:
• To expose various algorithms related to Deep Learning and Artificial Intelligence.
• To prepare students to apply suitable algorithm for the specified applications.

UNIT I

DEEP NETWORKS

9

Deep Networks: Modern Practices: Deep Forward Networks: Example: Learning XOR - Gradient-Based Learning - Hidden Units - Architecture Design - Regularization for Deep Learning.

UNIT II

MODELS

9

Optimization for Training Deep Models: How Learning Differs from Pure Optimization - Challenges in Neural Network Optimization - Basic Algorithms - Parameter Initialization Strategies - Algorithms with Adaptive Learning Rates - Approximate Second-Order Methods - Optimization Strategies and Meta- Algorithms.

UNIT III

INTELLIGENT SYSTEMS

9

Introduction to Artificial Intelligence: Intelligent Systems - Foundations of AI - Applications - Tic-Tac-Toe Game Playing - Problem Solving: State-Space Search and Control Strategies: Introduction - General Problem Solving - Exhaustive Searches - Heuristic Search Techniques.

UNIT IV

KNOWLEDGE REPRESENTATION

9

Advanced Problem-Solving Paradigm: Planning: Introduction - Types of Planning Systems - Knowledge Representation: Introduction - Approaches to Knowledge Representation - Knowledge Representation using Semantic Network - Knowledge Representation using Frames.

UNIT V

APPLICATIONS

9

Expert Systems and Applications: Blackboard Systems - Truth Maintenance Systems - Applications of Expert Systems - Machine-Learning Paradigms: Machine-Learning Systems - Supervised and Unsupervised Learnings.

TOTAL: 45 PERIODS

COURSE OUTCOMES:
1. Knowledge of Algorithms of Deep Learning & Artificial Intelligence.
2. Knowledge of applying Algorithm to specified applications.
3. Ability to understand intelligent systems and Heuristic Search Techniques
4. Understanding of Knowledge Representation, Semantic Networks and Frames
5. Knowledge Of Expert systems, applications and Machine learning

REFERENCES:
1. Ian Goodfellow, YoshuaBengio, Aaron Courville, “Deep Learning”, MIT Press, 2016.
2. Li Deng and Dong Yu, "Deep Learning Methods and Applications", Foundations and Trends in Signal Processing.
3. YoshuaBengio, "Learning Deep Architectures for AI", Foundations and Trends in Machine Learning.
4. SarojKaushik, "Artificial Intelligence", Cengage Learning India Pvt. Ltd.
5. Deepak Khemani, "A First Course in Artificial Intelligence", McGraw Hill Education(India) Private Limited, NewDelhi.
6. Elaine Rich, Kevin Night, Shivashankar B Nair, "Artificial Intelligence" Third Edition, McGraw Hill, 2008.

Comments

Popular posts from this blog

CS3491 Syllabus - Artificial Intelligence And Machine Learning - 2021 Regulation Anna University

BE3251 - Basic Electrical and Electronics Engineering (Syllabus) 2021-regulation Anna University

CS3401 Syllabus - Algorithms - 2021 Regulation Anna University