CSE275 Units
Study Units
Introduction to Optimization in Machine Learning
Evolutionary Algorithms
Swarm Intelligence Techniques
Advanced Nature-Inspired Optimization
Optimization for Machine Learning and AutoML
Hybrid Optimization and Applications
Continuous Assessment
2 components
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 6To 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 11Exams & Practice
Mid Term Examination
Mid-semester comprehensive evaluation
20%End Term Examination
Final semester comprehensive evaluation
50%Type: Examination
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.