BCA Machine Learning

bcasemester 8Elective

This course presents comprehensive introduction to several topics on basic concepts and techniques of Machine Learning (ML). It also explores the understanding of the Supervised and unsupervised learning techniques, probability based learning techniques, performance evaluation of ML algorithms and applications of ML.

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Chapter Wise Notes Question Banks Syllabus Chapter Wise Questions Viva Practical

Unit 1:Introduction to machine learning

History of machine learning, Brain-neuron learning system, Definition and types of learning, need of machine learning, Data and tools, review of statistics, training, validation and test data, theory of learning – feasibility of learning – error and noise – training versus testing, generalization bound – approximation-generalization tradeoff – bias and variance – learning curve

Unit 2:Introduction to Supervised Learning

Classification problems, Linear Regression- Predicting numerical value, Finding best fit line with linear regression, Perceptron, learning neural networks structures, Decision tree representation, appropriate problems for decision tree learning, basic decision tree algorithm, support vector machines, Separating data with maximum margin, Finding the maximum margin

Unit 3:Bayesian and instance based learning

Probability theory and Bayes rule, Classifying with Bayes decision theory, Conditional Probability, Bayesian Belief Network, K-nearest neighbor