# 17EC834 Machine learning – ML VTU Notes

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### 17EC834 Machine learning – ML VTU CBCS Notes

Here you can download the VTU CBCS 2017 Scheme notes, and Study materials of Machine learning – ML VTU of the Electronics and Communication Engineering department.

University Name: Visvesvaraya Technological University (VTU), Belagavi

Branch Name: Electronics and Communication Engineering – ECE

Semester: 8 (4th Year)

Subject Code and Subject Name: 17EC834 Machine learning – ML

Scheme of Examination: 2017 Scheme

## Machine Learning Video Tutorials

Important Concepts discussed:

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.”

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.”

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.”

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.”

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 the error of two hypotheses, paired t-Tests, Comparing two learning algorithms.”

Click the below link to download the 2017 Scheme VTU CBCS Notes of 17EC834 Machine learning – ML

M1, M2, M3, M4, and M5 Another Seet M2, M3, M4, and M5