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CSE274 Units

Practice

APPLIED MACHINE LEARNING

CSE274 3 Credits L:2 T:0 P:2 Minor Machine Learning

Through this course, students learn to explain types of data and data pre-processing concepts, apply feature engineering, and use dimensionality reduction techniques. They also learn to compare and implement nonlinear, regression, ensemble, and clustering models using appropriate evaluation metrics.

Study Units

Unit 1

Data Pre-processing

Notes MCQ Subjective
Unit 2

Feature Engineering and Dimensionality Reduction

Notes MCQ Subjective
Unit 3

Linear & Probabilistic Classification Models

Notes MCQ Subjective
Unit 4

Regression

Notes MCQ Subjective
Unit 5

Ensemble Learning

Notes MCQ Subjective
Unit 6

Unsupervised Learning

Notes MCQ Subjective

Continuous Assessment

2 components

Project 66.66%

To check the student learnability of Applied ML. To implement all the topics in a practical way

Week 3 / 12
Test - Code based 33.34%

To check the student learnability of Applied ML. Syllabus will be UNIT-1,2,and 3

Week 5 / 6

Exams & Practice

Mid Term Examination

Mid-semester comprehensive evaluation

20%
Coming Soon

End Term Examination

Final semester comprehensive evaluation

50%

Type: Examination

Coming Soon

Quick Navigation for CSE274

Unit 1 Notes Unit 2 Notes Unit 3 Notes Unit 4 Notes Unit 5 Notes

CSE274 - FAQs

How many units are in CSE274?

CSE274 has 6 units. Each unit includes detailed notes and MCQ practice questions.

What exam resources are available for CSE274?

Unit-wise notes and MCQ practice are available. Exam resources coming soon.

How to prepare for CSE274 exams?

Study each unit's notes thoroughly, practice MCQs to test understanding, and attempt mock tests before exams. Focus on important topics and previous year questions.