All Materials regarding to B.tech R-20 in Mechanical Engineering in 3rd Year 2nd Semester with unit wise for Every Subjects are available.
👇👇Scroll Down for Important Questions and Topics Unit Wise 👇👇
| Mech 3-2 Introduction to AI ML | ||
|---|---|---|
| S.No | Chapters / Units | Download Link |
| 1 | Unit 1 | Download |
| 2 | Unit 2 | Download |
| 3 | Unit 3 | Download |
| 4 | Unit 4 | Download |
| 5 | Unit 5 | Download |
Mech 3-2 Introduction to AI ML Important Topics Questions
UNIT– I:
Introduction: Definition of Artificial Intelligence, Evolution, Need, and applications in real world.
Intelligent Agents, Agents and environments; Good Behavior-The concept of rationality, the nature
of environments, structure of agents.
Neural Networks and Genetic Algorithms: Neural network representation, problems, perceptrons,
multilayer networks and back propagation algorithms, Genetic algorithms.
UNIT– II:
Knowledge–Representation and Reasoning: Logical Agents: Knowledge based agents, the
Wumpus world, logic. Patterns in Propositional Logic, Inference in First-Order Logic-Propositional
vs first order inference, unification and lifting
UNIT– III:
Bayesian and Computational Learning: Bayes theorem , concept learning, maximum likelihood,
minimum description length principle, Gibbs Algorithm, Naïve Bayes Classifier, Instance Based
Learning- K-Nearest neighbour learning
Introduction to Machine Learning (ML): Definition, Evolution, Need, applications of ML in
industry and real world, classification; differences between supervised and unsupervised learning
paradigms.
UNIT– IV:
Basic Methods in Supervised Learning: Distance-based methods, Nearest-Neighbors, Decision
Trees, Support Vector Machines, Nonlinearity and Kernel Methods.
Unsupervised Learning: Clustering, K-means, Dimensionality Reduction, PCA and kernel.
UNIT– V:
Machine Learning Algorithm Analytics: Evaluating Machine Learning algorithms, Model,
Selection, Ensemble Methods (Boosting, Bagging, and Random Forests).
Modeling Sequence/Time-Series Data and Deep Learning: Deep generative models, Deep
Boltzmann Machines, Deep auto-encoders, Applications of Deep Networks.
TEXT BOOKS:
1) Stuart Russell and Peter Norvig, Artificial Intelligence: A Modern Approach, 2/e, Pearson
Education, 2010.
2) Tom M. Mitchell, Machine Learning, McGraw Hill, 2013.
3) Ethem Alpaydin, Introduction to Machine Learning (Adaptive Computation and Machine
Learning), The MIT Press, 2004.
REFERENCE BOOKS:
1) Elaine Rich, Kevin Knight and Shivashankar B. Nair, Artificial Intelligence, 3/e, McGraw Hill
Education, 2008.
2) Dan W. Patterson, Introduction to Artificial Intelligence and Expert Systems, PHI Learning,
2012.
3) T. Hastie, R. Tibshirani, J. H. Friedman, The Elements of Statistical Learning, 1/e, Springer,
2001.
4) Bishop, C. M., Pattern Recognition and Machine Learning, Springer, 2006.
5) M. Narasimha Murty, Introduction to Pattern Recognition and Machine Learning, World
Scientific Publishing Company, 2015
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