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Tags: Artificial Intelligence and Machine Learning, Engineering/Computer Science Electronics\Communication and Instrumentation Engineering, Ela kumar
Artificial Intelligence and Machine Learning
- Author : Lyla B. Das & Sudhish N. George & Anup Aprem
This book is designed for undergraduates, postgraduates and professionals who want to have a firm grip on the fundamental principles of AI and ML.
Artificial Intelligence (AI) is a broad area of knowledge which has percolated into every aspect of human life. ‘Machine Learning algorithms’ are considered to be a subset of AI Theory, mathematics and coding are three aspects to any topic in AI. This book covers the most relevant topics in the field of Artificial Intelligence and Machine Learning (ML).
The subdivisions of Machine Learning are Supervised, Unsupervised and Reinforcement learning. All three are covered in sufficient depth. One very important and upcoming field of application is Natural Language Processing (NLP). A whole section of the book has been devoted to this.
The book covers the conceptual, mathematical and numerical analysis of the important ML algorithms and their practical applications. The topics covered include AI search algorithms, Classical machine learning, Deep learning theory and popular networks, Natural Language Processing (NLP) and Reinforcement learning. Numerical examples and lucid explanations give the reader an easy entry into the world of AI and ML.
Part I: Introduction, Classical ML, Mathematical Foundations and AI Search Algorithms
1. Artificial Intelligence (AI) and Machine Learning (ML): An Introduction
2. The Machine Learning (ML) Process
3. Classical Machine Learning Algorithms-I
4. Classical Machine Learning Algorithms-II
5. Bayesian Classifier
6. Parametric and Non-Parametric Estimation Techniques
7. Unsupervised Learning & Clustering
8. Dimensionality Reduction Techniques
9. Search Algorithms in AI
Part II: Deep Learning
10. Deep Learning Basics: Artificial Neural Networks, Backpropagation and Further Optimizations
11. Convolutional Neural Network (CNN)
12. Sequence Modeling: RNN, LSTM, GRU
13. Generative Adversarial Network (GAN)
Part III: Natural Language Processing (NLP)
14. Introduction to Natural Language Processing and Text Pre-processing
15. Statistical Language Models
16. Frequency Based Methods
17. Prediction Based Methods for Word Embeddings: Word2vec
18. Applications of NLP Using Deep Learning Techniques
Part IV: Reinforcement learning
19. Reinforcement Learning: Introduction, Motivation and Brief History
20. Markov Decision Process
21. Reinforcement Learning: Monte Carlo and Temporal Difference Methods
22. Reinforcement Learning: Policy Gradient Methods
23. Partially Observable Markov Decision Process (POMDP)
Appendix A: Linear Algebra
Appendix B: Python Programming
Bibliography
Index
• Clear and lucid explanations of concepts with in-depth analysis
• Numerical examples to ensure clear understanding
• Critical evaluation of latest applications
• Inclusion of coding
• Elaborate coverage of Deep Learning Networks such as CNN, RNN and GAN
• Step by step coverage of NLP processes and applications
• Exclusive chapter on Attention mechanisms and Transformers
• Extensive coverage of Reinforcement Learning algorithms
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