| Course Code: CS2301 Course Name: Machine Learning Prerequisites: NIL Syllabus: Introduction to machine learning: Learning paradigms and applications, supervised vs unsupervised learning, training and test data, generalization and overfitting, model evaluation metrics; Models for Regression: Linear regression, least squares estimation, basis function expansion, bias-variance trade-off, regularization methods including ridge regression; Models for Classification: Classification framework, decision boundaries, logistic regression, multiclass softmax regression, discriminative vs generative classification, perceptron learning algorithm, regularization and model selection, KNN classification, Decision trees; Neural Networks: Multilayer perceptrons, network architectures, activation functions, forward propagation, backpropagation learning algorithm, gradient descent optimization, regularization and generalization in neural networks; Kernel Methods: Kernel functions, kernel trick, feature space transformations, support vector machines, margin maximization, kernel regression, kernel classification, sparse kernel machines and relevance vector machines; Unsupervised Learning and Dimensionality Reduction: Clustering methods including k-means, vector quantization, principal component analysis (PCA), probabilistic PCA; Introduction to Reinforcement Learning; Texts: 1. Christopher M. Bishop, Pattern Recognition and Machine Learning, Springer NP Exclusive (CBS), 2009. 2. Kevin P. Murphy, Machine Learning: A Probabilistic Perspective, The MIT Press, 2012. 3. Tom M. Mitchell, Machine Learning, McGraw-Hill Education, 2017 |