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CST396 previous year question papers
CST396 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, auto encoders, sampling methods and PAC learning. This course helps the students to provide machine learning based solutions to real world problems.
Prerequisite: Basic understanding of probability theory, linear algebra, multivariate calculus and multivariate probability theory.
Course outcomes
| CO | Outcome | Level |
|---|---|---|
| CO1 | Illustrate the concepts of regression and classification techniquesApply | Apply |
| CO2 | Demonstrate various unsupervised learning techniquesApply | Apply |
| CO3 | Choose suitable model parameters for different machine learning techniques and to evaluate a model performanceApply | Apply |
| CO4 | Explain the framework of PAC learning, basic concepts of VC dimension and non- uniform learnabilityUnderstand | Understand |
| CO5 | Construct Bayesian models for data and apply computational techniques to draw inferencesApply | Apply |
| CO6 | Illustrate the concepts of sampling algorithms, auto encoder, generative adversarial networksApply | Apply |
Departments that study CST396
- COMPUTER SCIENCE AND DESIGN (S5)
- Computer Science Engineering (S5)
- Computer Science and Engineering (Cyber Security) (S4)
Frequently asked questions
Which KTU scheme is CST396 in?
Advanced Topics in Machine Learning (CST396) is in the KTU 2019 scheme and is a VAC course.
How many credits is Advanced Topics in Machine Learning?
Advanced Topics in Machine Learning (CST396) carries 4 credits.
Which semester is CST396 taught in?
CST396 is taught in S4, S5. The semester differs between departments.
Where can I download CST396 previous year question papers?
This page lists 1 CST396 question paper. Select them and download as a ZIP or one merged PDF. It is free and needs no sign-up.
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