1.To provide Knowledge on Probability distributions.2. To study the algebra and its uses in solving Eigen value problems and single value decomposition problems.3. To explain the concepts in number theory.4. To predict a probability distribution over a set of classes given an observation of input, instead of simply outputting the most likely class to which the observation should belong.5. The objective of a simple regression course is to teach students the fundamental concepts and techniques of regression analysis, focusing on models with one independent variable.6. This includes understanding the assumption of regression, interpreting regression coefficients, assessing model fit, making predictions and using regression for inference and hypothesis testing.7. Support Vector Machine Terminology.8. Unsupervised Machine Learning and Divisive clustering.9. EM Algorithm in Machine Learning ( Expectation- Maximization).10. Emphasizes Decision Trees with Pre -Pruning and Post -Pruning Techniques. Showcases Random Forest and its Applications.11. Explore various types of perceptron and Neural Networks. Details Convolutional Neural Networks with Step- by- step Implementation.12. Examines Recurrent Neural networks including their Types, Architectures and Applications.13. Delves into K -Nearest Neighbors, Learning Locally Weighted Regression Learning and Radio Basis Function.
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N. Bhaskar
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K. L. Vasundhara
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Monalisha Pattnaik
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Mahesh Kumar
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