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4TH YEAR CSE
MACHINE LARNING AND Artificial intelligence Comprehensive Overview of Machine Learning Concepts and Techniques, Understanding Supervised Learning: From Basics to Applications Exploring Model Selection and Generalization in Machine Learning Supervised vs Unsupervised Learning: A Comparative Analysis Delving into Vapnik-Chervonenkis Dimensions: Theoretical Insights Bayesian Decision Theory: Framework for Uncertain Environments Bias and Variance Estimators: Balancing Model Complexity Model Selection Procedures: A Step-by-Step Guide Maximum Likelihood Estimation: Multinomial and Gaussian Densities Multivariate Classification: Handling Multiple Features and Classes Multivariate Normal Distribution: Probabilistic Framework Future Selection Techniques: Subset, Forward, and Backward Understanding Clustering Techniques: Spectral vs Hierarchical Exploring Nonparametric Classification Methods with Examples Key Concepts in Distance Learning and Nearest Neighbor Approaches In-depth Analysis of Gradient Descent and Decision Trees Generalizing Linear Models and Applications Unveiling the Power of Neural Networks: Backpropagation and Perceptron Multi-Layer Perceptron (MLP): Universal Approximator and Parallel Processing
4TH YEAR CSE
MACHINE LARNING AND AIRTIFICIAL INTILEGENCE Comprehensive Guide to Multivariate Data Analysis: Estimation, Multivariate Normal Distribution, Multivariate Classification, Multivariate Regression, Feature Selection, Linear Discriminant Analysis, Canonical Correlation Analysis, Feature Embedding, Multidimensional Scaling, Singular Value Decomposition, Matrix Factorization
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