Machine Learning For MU Semester 6 AIDS,CSE(DS)CSE(AIML)AIML,DE (Code :CSC604 Academic Year 2022-2023  (Paperback, Dr. Vaikole Shubhangi L, Prof. R. M. Baphana)

1 Introduction to Machine Learning 6 1.1 Introduction to Machine Learning, Issues in Machine Learning, Application of Machine Learning, Steps of developing a Machine Learning Application. 1.2 Supervised and Unsupervised Learning : Concepts of Classification, Clustering and prediction, Training, Testing and validation dataset, cross validation, overfitting and under fitting of model. (Refer Chapter 1) 1.3 Performance Measures : Measuring Quality of model - Confusion Matrix, Accuracy, Recall, Precision, Specificity, F1 Score, RMSE. 2 Mathematical Foundation for ML 5 2.1 System of Linear equations, Norms, Inner products, Length of Vector, Distance between vectors, Orthogonal vectors. 2.2 Symmetric Positive Definite Matrices, Determinant, Trace, Eigenvalues and vectors, Orthogonal Projections, Diagonalization, SVD and its applications. (Refer Chapter 2) 3 Linear Models 7 3.1 The least-squares method, Multivariate Linear Regression, Regularised Regression, Using Least-Squares Regression for classification. 3.2 Support Vector Machines. (Refer Chapter 3) 4 Clustering 4 4.1 Hebbian Learning rule. 4.2 Expectation -Maximization algorithm for clustering. (Refer Chapter 4) 5 Classification Models 12 5.1 Introduction, Fundamental concept, Evolution of Neural Networks, Biological Neuron, Artificial Neural Networks, NN architecture, McCulloch-Pitts Model. Designing a simple network, Non-separable patterns, Perceptron model with Bias. Activation functions, Binary, Bipolar, continuous, Ramp. Limitations of Perceptron. 5.2 Perceptron Learning Rule. Delta Learning Rule (LMS -Widrow Hoff), Multilayer perceptron network. Adjusting weights of hidden layers. Error back propagation algorithm. 5.3 Logistic regression. (Refer Chapter 5) 6 Dimensionality Reduction 5 6.1 Curse of Dimensionality. 6.2 Feature Selection and Feature Extraction. 6.3 Dimensionality Reduction Techniques, Principal Component Analysis. (Refer Chapter 6) Total 39