## 15CS73 Machine Learning VTU Notes

Download VU CBCS notes of 15CS73 Machine Learning VTU Notes for 7th-semester computer science and engineering, VTU Belagavi.

### Module 1 – Introduction to Machine Learning and Concept Learning

Following are the contents of module 1 – Introduction to Machine Learning and Concept Learning

Introduction to Machine Learning. Learning problems and Designing a Learning system. Different Perspectives and Machine Learning issues.

Introduction to Concept Learning and Concept learning. Concept learning as a search of a hypothesis. Find-S and Candidate Elimination algorithm. Version space, Inductive Bias of Find-S and Candidate Elimination algorithm.

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**Module 1 – 15CS73 Machine Learning VTU Notes**

### Module 2 – Decision Tree Learning

Following are the contents of module 2 – Decision Tree Learning

Introduction to Decision Tree Learning Algorithm. Decision tree representation and appropriate problems for

decision tree learning. The decision Tree Learning Hypothesis space search, Inductive bias, and Issues in decision tree learning algorithm.

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**Module 2 – 15CS73 Machine Learning VTU Notes**

### Module 3 – Artificial Neural Networks

Following are the contents of module 3 – Artificial Neural Networks

Introduction to Artificial Neural Networks. Artificial Neural Network representation, appropriate problems Artificial Neural Network, Perceptrons, a sigmoid function, Back-propagation algorithm, and its derivation.

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**Module 3 – 15CS73 Machine Learning VTU Notes**

### Module 4 – Bayesian Learning

Following are the contents of module 4 – Bayesian Learning

Introduction to Bayesian Learning. Bayes theorem and its concept learning, Minimum Description Length principle. Introduction to Naive Bayes classifier and numerical example, Bayesian belief networks, and EM, K-means algorithm.

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**Module 4 – 15CS73 Machine Learning VTU Notes**

### Module 5 – Evaluating Hypothesis, Instance-Based and Reinforcement Learning

Following are the contents of module 5 – Evaluating Hypothesis, Instance-Based and Reinforcement Learning

Introduction to Evaluating Hypothesis. Basics of the sampling theorem, General approach for deriving confidence intervals, calculating the difference in error of two hypothesis, paired t-Tests, Comparing two learning algorithms.

Introduction to Instance-Based Learning. the k-nearest neighbor learning algorithm, locally weighted regression algorithm, radial basis function, case-based reasoning algorithm.

Introduction to Reinforcement Learning and Q Learning algorithm.

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**Module 5 – 15CS73 Machine Learning VTU Notes**

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