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17CS73 / 15CS73 Machine Learning VTU Notes
Download VU CBCS notes of 17CS73 / 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.
Concept Learning in Machine Learning
Find-S Algorithm Machine Learning and Unanswered Questions of Find-S Algorithm
Find-S Algorithm – Maximally Specific Hypothesis and Solved Example – 1 and Solved Example -2
Consistent Hypothesis, Version Space and List Then Eliminate algorithm Machine Learning
Candidate Elimination Algorithm and Solved Example – 1 Machine Learning
Candidate Elimination Algorithm and Solved Example – 2 Machine Learning
Candidate Elimination Algorithm and Solved Example – 3 Machine Learning
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.
1. How to build a decision Tree for Boolean Function Machine Learning
2. How to build a decision Tree for Boolean Function Machine Learning
3. How to build Decision Tree using ID3 Algorithm – Solved Numerical Example – 1
4. How to build Decision Tree using ID3 Algorithm – Solved Numerical Example -2
5. How to build Decision Tree using ID3 Algorithm – Solved Numerical Example -3
6. Appropriate Problems for Decision Tree Learning Machine Learning Big Data Analytics
7. How to find the Entropy and Information Gain in Decision Tree Learning
8. Issues in Decision Tree Learning Machine Learning
9. How to Avoid Overfitting in Decision Tree Learning, Machine Learning, and Data Mining
10. How to handle Continuous Valued Attributes in Decision Tree Learning, Machine Learning
Module 3 – Artificial Neural Networks
Following are the contents of module 3 – Artificial Neural Networks
Appropriate problems which can be solved using Artificial Neural Networks – Machine Learning
Perceptron Training Rule for Linear Classification – Artificial Neural Network
AND GATE Perceptron Training Rule – Artificial Neural Network
OR GATE Perceptron Training Rule – Artificial Neural Network
1. Gradient Descent and Delta Rule, Derivation of Delta Rule, Linealry and Non-linearly Separable Data
2. Gradient Descent Algorithm and the Delta Rule for Non-Linearly Separable Data
Introduction to Artificial Neural Networks. Artificial Neural Network representation, appropriate problems Artificial Neural Network, Perceptrons, a sigmoid function, Back-propagation algorithm, and its derivation.
Module 4 – Bayesian Learning
Following are the contents of module 4 – Bayesian Learning
Naive Bayes Theorem, Maximum A Posteriori Hypothesis, MAP Brute Force Algorithm
Maximum Likelihood Hypothesis and Least Squared Error Hypothesis
How to use Naive Bayes rule to check whether the Patient has Cancer or Not
1. Solved Example Naive Bayes Classifier to classify New Instance PlayTennis
2. Solved Example Naive Bayes Classifier to classify New Instance, Species Class M and H
3. Solved Example Naive Bayes Classifier to classify New Instance Car Example
4. Solved Example Naive Bayes Classifier to classify New Instance Football Match Example
1. Bayesian Belief Network (BBN) Solved Numerical Example Burglar Alarm System
2. Bayesian Belief Network (BBN) Solved Numerical Example Burglar Alarm System
KMeans Clustering Algorithm, Steps in KMeans Algorithm, Advantages and Disadvantages
Expectation-Maximization EM Algorithm Steps Uses Advantages and Disadvantages
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.
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 the error of two hypotheses, 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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M1, M2, M3, M4 and M5 Another Seet M2, M3, M4 and M5
Download PPT – M1, M2, M3, M4 and M5
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