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Quick Facts
| Medium Of Instructions | Mode Of Learning | Mode Of Delivery |
|---|---|---|
| English | Self Study | Video and Text Based |
Courses and Certificate Fees
| Fees Informations | Certificate Availability | Certificate Providing Authority |
|---|---|---|
| INR 2188 | yes | Coursera |
The Syllabus
Videos
- Probability and Conditional Probability
- Bayesian Reasoning, and Independence
- Formal Description of NBC and examples
Readings
- Learning Classifiers based on Bayes Rule
- Naive Bayes for Continuous Inputs
- Regularization
Practice Exercises
- Quiz 01
- Quiz 02
- Quiz 03
Videos
- Linear Support Vector Machine
- Dual Form for Learning LSVM and Linear SVM with Soft Margin
- Basic Idea
Readings
- Kernel Methods
- Support Vector Machines
- Optimal margin classifiers
Practice Exercises
- Quiz 04
- Quiz 05
- Quiz 06
Videos
- What is the key idea of Decision Tree?
- Regression Tree and Random Forests
- Regression Tree and Random Forests
Readings
- Introduction
- A connection to game theory and linear programming
- Experiments and applications
Practice Exercises
- Quiz 07
- Quiz 08
- Quiz 09
Videos
- Introduction, k-means and k-medoids
- Simple Example of k-Means, Hierarchical Method
- What is the Gaussian Mixture Model?
Readings
- Approximation algorithms for k-means and k-median
- Clustering of stable instances
- Mixture Models
Practice Exercises
- Quiz 10
- Quiz 11
- Quiz 12
- Final Test
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