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INT423 Units

Practice

MACHINE LEARNING-II

INT423 3 Credits L:2 T:0 P:2 Minor Machine Learning

This course covers unsupervised learning concepts including K-Means and advanced clustering algorithms, anomaly detection methods, reinforcement learning principles such as MDPs and Deep Q-Networks, and the development of recommender systems.

Study Units

Unit 1

Introduction to Unsupervised Learning & K-Means

Notes MCQ
Unit 2

Advanced Clustering Techniques

Notes MCQ
Unit 3

Clustering Metrics

Notes MCQ
Unit 4

Foundations of Reinforcement Learning

Notes MCQ
Unit 5

Q-Learning & Deep Q- Networks

Notes MCQ
Unit 6

Recommender Systems

Notes MCQ

Continuous Assessment

2 components

Test - Code based 50%

Student will be able to enhance the programming skills and capable to work on projects which is based on the real world problems. The syllabus covers units 1-2 for preparation of code based CA.

Week 5
Project 50%

Projects will be assigned individually to students in the 3rd week and submission/evaluation will be in 12th week. To make the students familiar with real time problems and to enhance their project skills.

Week 12

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 INT423

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

INT423 - FAQs

How many units are in INT423?

INT423 has 6 units. Each unit includes detailed notes and MCQ practice questions.

What exam resources are available for INT423?

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

How to prepare for INT423 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.