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CST413 previous year question papers
CST413 syllabus
This course enables the learners to understand the advanced concepts and algorithms in machine learning. The course covers the standard and most popular supervised learning algorithms such as linear regression, logistic regression, decision trees, Bayesian learning and the Naive Bayes algorithm, basic clustering algorithms and classifier performance measures. This course helps the students to provide machine learning based solutions to real world problems.
Prerequisite: Basic understanding of probability theory, linear algebra and Python Programming
Course outcomes
| CO | Outcome | Level |
|---|---|---|
| CO1 | Illustrate Machine Learning concepts and basic parameter estimation methods.Apply | Apply |
| CO2 | Demonstrate supervised learning concepts (regression, linear classification).Apply | Apply |
| CO3 | Illustrate the concepts of Multilayer neural network and Support Vector MachineApply | Apply |
| CO4 | Describe unsupervised learning concepts and dimensionality reduction techniques.Apply | Apply |
| CO5 | Solve real life problems using appropriate machine learning models and evaluate the performance measuresApply | Apply |
Departments that study CST413
- Computer Science Engineering (S7)
- Computer Science and Engineering (Cyber Security) (S7)
- Electrical and Computer Engineering (S7)
- Cyber Security (S7)
Frequently asked questions
Which KTU scheme is CST413 in?
Machine Learning (CST413) is in the KTU 2019 scheme and is a PEC course.
How many credits is Machine Learning?
Machine Learning (CST413) carries 3 credits.
Which semester is CST413 taught in?
CST413 is taught in S7.
Where can I download CST413 previous year question papers?
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