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OPTIMIZATION TECHNIQUES FOR MACHINE LEARNING

CSE275 3 Credits L:2 T:0 P:2 Minor Machine Learning

This subject explores optimization techniques in machine learning and artificial intelligence systems, covering population-based, evolutionary, swarm intelligence, hybrid, and metaheuristic algorithms. It focuses on applying these methods for feature selection, model optimization, hyperparameter tuning, and developing real-world solutions.

Study Units

Unit 1

Introduction to Optimization in Machine Learning

Unit 2

Evolutionary Algorithms

Unit 3

Swarm Intelligence Techniques

Unit 4

Advanced Nature-Inspired Optimization

Unit 5

Optimization for Machine Learning and AutoML

Unit 6

Hybrid Optimization and Applications

Continuous Assessment

2 components

Test 50%

This assessment aims to measure students' comprehension of key optimization concepts, including gradient-based and gradient-free techniques, evolutionary algorithms, and swarm intelligence methods. The test includes both theoretical and applied components – short-answer questions, problem-solving exercises, and case-based scenarios.

Week 6
Project 50%

To enable students to apply theoretical knowledge of optimization algorithms to practical machine learning problems. Students are required to identify a suitable dataset, define an optimization objective (e.g., feature selection, hyperparameter tuning, or model performance enhancement), and design an algorithmic solution using evolutionary, swarm, or hybrid optimization techniques.

Week 11

Exams & Practice

Mid Term Examination

Mid-semester comprehensive evaluation

20%
Coming Soon

End Term Examination

Final semester comprehensive evaluation

50%

Type: Examination

Coming Soon

CSE275 - FAQs

How many units are in CSE275?

CSE275 has 6 units. Each unit includes detailed notes and MCQ practice questions.

What exam resources are available for CSE275?

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

How to prepare for CSE275 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.