The book has been primarily designed for the beginners in the subject. It has been written from the students' perspective, making it easy to understand. The contents are briefly explained with the help of examples in a direct and a pragmatic approach. Each chapter begins with the basics and is standalone; the dependence of the chapters on previous concepts has been minimized. The text is aimed to balance the mix of notation and words in mathematical statements. Artificial Intelligence and Soft Computing topics are often expressed in terms of algorithms, hence key algorithms are introduced with their explanations. These algorithms are expressed in words and in an easy to understand form of structured psuedocodes. The students should easily grasp the psuedocodes used in the text to express the algorithms, regardless of whether they have formally studied programming languages.
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Anuj Sharma is an academician and researcher in the field of Computer Science. He currently holds the position of Associate Professor at Panjab University, Chandigarh, where he teaches postgraduate courses in Computer Science. Dr. Sharma received his Post-doc. from RWTH Aachen, Germany and PhD in Computer Science from Thapar Institute of Engineering and Technology, Patiala. His PhD thesis is on the topic “Online Handwriting Recognition in the areas of Pattern Recognition and Machine Learning”. He currently teaches courses in Artificial Intelligence, Soft Computing, Discrete Mathematical Structures, Machine Learning, theory of Computation and conducts practicals for programming. His publications include numerous articles in professional journals in the areas of Artificial Intelligence, Document Analysis and Recognition, Machine Learning, Optimization and Computational Methods. He has also presented his research work in leading conferences. Also, he serves on the editorial board of several reputed journals and reviewed papers of many leading journals. Dr. Sharma is also interested in programming related problems of real-life use, mainly in Pattern Recognition and Machine Learning. He also works in the area of Optimization in transportation and assignment related problems and has developed various codes in Python, Java, VC++ for many Pattern Recognition and Machine Learning projects.
1. Introduction * 2. State-Space Representation * 3. Search Techniques * 4. Knowledge Representation and Resolution Principle * 5. Expert Systems * 6. Soft Computing * 7. Artificial Neural Networks * 8. Classification Techniques * 9. Clustering Techniques * 10. Dimension Reduction Techniques * 11. Fuzzy Logic Systems * 12. Genetic Algorithms
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