MI4026 Deep Learning and Artificial Intelligence Syllabus:
MI4026 Deep Learning and Artificial Intelligence Syllabus – Anna University PG Syllabus Regulation 2021
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
Deep Networks: Modern Practices: Deep Forward Networks: Example: Learning XOR – Gradient-Based Learning – Hidden Units – Architecture Design – Regularization for Deep Learning.
UNIT II MODELS
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
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
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
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.