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INT255 — Mathematics Behind Machine Learning

Practice

Course Overview

INT255 3 Credits L:2 T:0 P:2 Minor Machine Learning

This course aims to equip students with the mathematical foundations necessary for understanding and applying machine learning models, covering topics such as linear algebra, probability, optimization techniques, and regularization methods.

Study Units

Unit 1

Linear Algebra Foundations for Machine Learning

Notes MCQ Subjective
Unit 2

Matrix Decompositions for Representation Learning

Notes MCQ Subjective
Unit 3

Probability Theory and Loss Functions for Machine Learning

Notes MCQ Subjective
Unit 4

Optimization Techniques for Machine Learning

Notes MCQ Subjective
Unit 5

Mathematical Foundations of Support Vector Machines

Notes MCQ Subjective
Unit 6

Regularization and Generalization Theory

Notes MCQ Subjective

Continuous Assessment

2 components

All 2 CAs count towards your CA score.
Test 1 50%

To assess individual student learning outcomes through a Subjective Test.

Week 5 / 6
Test 2 50%

To measure and check for individual student understanding of the subject through a Subjective Test.

Week 10 / 12

Exams & Practice

Mid Term Examination

Mid-semester comprehensive evaluation

20%

End Term Examination

Final semester comprehensive evaluation

50%

Type: Examination

INT255 - FAQs

How many units are in INT255?

INT255 has 6 units. Each unit includes detailed notes and MCQ practice questions.

What exam resources are available for INT255?

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

How to prepare for INT255 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.