Machine Learning 3170724 Syllabus Download With Weightage
Machine Learning 3170724 is a term that refers to Computer Department covers this subject This year, this Subject is covered in the 7th Semester.
Sr. No.
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Content
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Total
Weightage
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| 1 | Introduction to Machine Learning: Overview of Human Learning and Machine Learning, Types of Machine Lear |
4 |
| 2 | Preparing to Model: Machine Learning activities, Types of data in Machine Learning, Structures of data, Data quality and remediation, Data Pre-Processing: Dimensionality reduction, Feature subset selection. |
6 |
| 3 | Modelling and Evaluation: Selecting a Model: Predictive/Descriptive, Training a Model for supervised learning, model representation and interpretability, Evaluating performance of a model, Improving performance of a model |
8 |
| 4 | Basics of Feature Engineering: Feature and Feature Engineering, Feature transformation: Construction and extraction, Feature subset selection : Issues in high-dimensional data, key drivers, measure and overall process |
5 |
| 5 | Overview of Probability: Statistical tools in Machine Learning, Concepts of probability, Random variables, Discrete distributions, Continuous distributions, Multiple random variables, Central limit theorem, Sampling distributions, Hypothesis testing, Monte Carlo Approximation |
6 |
| 6 | Bayesian Concept Learning: Impotence of Bayesian methods, Bayesian theorem, Bayes’ theorem and concept learning, Bayesian Belief Network |
8 |
| 7 | Supervised Learning: Classification and Regression: Supervised Learning, Classification Model, Learning steps, Classification algorithms, Regression, Regression algorithms, |
15 |
| 8 | Unsupervised Learning: Supervised vs. Unsupervised Learning, Applications, Clustering, Association rules |
9 |
| 9 | Neural Network: Introduction to neural network, Biological and Artificial Neurons, Types of Activation functions, Implementation of ANN, Architecture, Leaning process, Backpropogation, Deep Learning |
9 |
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