CS3491 Artificial Intelligence and Machine Learning Notes
Download CS3491 Artificial Intelligence and Machine Learning Books, Lecture Notes, Part-A 2 marks with answers, Part-B 16 marks Questions, PDF Books. In this Notes Very Useful for Second Year Fourth Semester Students.
“CS3491 Artificial Intelligence and Machine Learning Books”
“CS3491 Artificial Intelligence and Machine Learning Lecture Notes”
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“CS3491 Artificial Intelligence and Machine Learning Notes”
Subject Info:
| Semester | Fourth Semester |
| Department | CSE |
| Year | Second Year |
| Regulation | R 2021 |
| Subject Code / Name | CS3491 Artificial Intelligence and Machine Learning |
| Content | Local Authors Books, Lecture Notes |
Syllabus:
CS3491 Artificial Intelligence and Machine Learning
UNIT I PROBLEM SOLVING
Introduction to AI – AI Applications – Problem solving agents – search algorithms – uninformed search strategies – Heuristic search strategies – Local search and optimization problems – adversarial search – constraint satisfaction problems (CSP)
UNIT II PROBABILISTIC REASONING
Acting under uncertainty – Bayesian inference – naïve bayes models. Probabilistic reasoning – Bayesian networks – exact inference in BN – approximate inference in BN – causal networks.
UNIT III SUPERVISED LEARNING
Introduction to machine learning – Linear Regression Models: Least squares, single & multiple variables, Bayesian linear regression, gradient descent, Linear Classification Models: Discriminant function – Probabilistic discriminative model – Logistic regression, Probabilistic generative model – Naive Bayes, Maximum margin classifier – Support vector machine, Decision Tree, Random forests
UNIT IV ENSEMBLE TECHNIQUES AND UNSUPERVISED LEARNING
Combining multiple learners: Model combination schemes, Voting, Ensemble Learning – bagging, boosting, stacking, Unsupervised learning: K-means, Instance Based Learning: KNN, Gaussian mixture models and Expectation maximization
UNIT V NEURAL NETWORKS
Perceptron – Multilayer perceptron, activation functions, network training – gradient descent optimization – stochastic gradient descent, error backpropagation, from shallow networks to deep networks –Unit saturation (aka the vanishing gradient problem) – ReLU, hyperparameter tuning, batch normalization, regularization, dropout.