AI-Powered Learning Platform

Subject Syllabus

CB-506 (C) - Machine Learning

CSBS · Semester 5

Course Modules

Learn topic-wise with Hyper AI and revise using mapped PYQs.

Introduction to Machine Learning (ML)Relationship between ML and human learning A quick survey of major models of how machines learn Example applications of ML

Supervised Learning The problem of classification Feature Engineering Training and testing classifier models Cross-validation Model evaluation (precision, recall, F1-measure, accuracy, area under curve)Statistical decision theory including discriminant functions and decision surfaces Naive Bayes classification Bayesian networks Decision Tree and Random Forests k-Nearest neighbor classification Support Vector Machines Artificial neural networks including backpropagation Applications of classifications Ensembles of classifiers including bagging and boosting

Hidden Markov Models (HMM) with forward-backward and Viterbi algorithms Sequence classification using HMM Conditional random fields Applications of sequence classification such as part-of-speech tagging

Multi-variable regression Model evaluation Least squares regression Regularization LASSO Applications of regression

Association rule mining algorithms including apriori Expectation-Maximization (EM) algorithm for unsupervised learning Clustering: Average linkage Ward's algorithm Minimum spanning tree clustering K-nearest neighbours clustering BIRCH CURE DBSCAN Anomaly and outlier detection methods