Introduction to Machine Learning – IITKGP

Week 1:Introduction: Basic definitions, types of learning, hypothesis space and inductive bias, evaluation, cross-validation
Week 2:Linear regression, Decision trees, overfitting
Week 3:Instance based learning, Feature reduction, Collaborative filtering based recommendation
Week 4:Probability and Bayes learning
Week 5:Logistic Regression, Support Vector Machine, Kernel function and Kernel SVM
Week 6:Neural network: Perceptron, multilayer network, backpropagation, introduction to deep neural network
Week 7:Computational learning theory, PAC learning model, Sample complexity, VC Dimension, Ensemble learning
Week 8:Clustering: k-means, adaptive hierarchical clustering, Gaussian mixture model