Скачать или смотреть NPTEL Machine Learning and Deep Learning – Fundamentals and Applications Week 3 Assignment Answers

NPTEL Machine Learning and Deep Learning – Fundamentals and Applications Week 3 Assignment Answers
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Course Code: NOC25-EE181
Course Title: Machine Learning and Deep Learning – Fundamentals and Applications
Instructor: Prof. M.K. Bhuyan
Institution: IIT Guwahati
Duration: 12 Weeks
Week: 3 Assignment Answers (July–December 2025 Session)

YouTube Title
NPTEL Machine Learning & Deep Learning Week 3 Assignment Answers | NOC25-EE181 | IIT Guwahati | Jul–Dec 2025

YouTube Description
Course: Machine Learning & Deep Learning – Fundamentals and Applications
Instructor: Prof. M.K. Bhuyan
Institution: IIT Guwahati
Session: NPTEL July–December 2025
Content: Clean, concept-rich walkthrough of Week 3 Assignment Solutions

What’s Inside
Detailed breakdown of Week 3 assignment solutions

Clear explanations of Density Estimation Techniques:

Parametric (e.g., Gaussian models)

Non-parametric methods such as Parzen Windows and K-Nearest Neighbors (KNN)

Real-world examples to illustrate density modeling

Comparison of strengths and limitations across different approaches

Practical tips to approach assignment-style questions

These topics align precisely with Week 3 of the course curriculum:
“Parametric and Non-Parametric Density Estimation — ML and Bayesian Estimation, Parzen Window, and KNN.”
NPTEL Online Courses

Ideal For
Students aiming to master statistical foundations in ML

Engineering, AI, and data science learners

NPTEL certificate aspirants targeting Elite/Gold

Learners preparing for exams or project-based assignments

Anyone looking to understand density estimation with clarity and applications

Hashtags (comma-separated)
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