🧠 Machine Learning for Electronics Engineers (EEE G513) – Lab Assignments

This repository contains lab assignments and solutions for the course
EEE G513: Machine Learning for Electronics Engineers
at Birla Institute of Technology and Science, Pilani – Hyderabad Campus.

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📘 Course Overview

The course provides a conceptual foundation of Machine Learning (ML) with a focus on its applications in electronic systems. It covers supervised, unsupervised, semi-supervised, and reinforcement learning, with emphasis on efficient architectures and topologies suitable for hardware and embedded implementation.

Instructor: R. Venkateswaran
Semester: Aug–Dec 2025

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🎯 Learning Outcomes

• Apply ML techniques to model, design, validate, and optimize electronic systems.
• Gain practical exposure to implementing ML algorithms in Python.
• Perform performance evaluation of ML models using proper metrics.
• Build foundational skills for advanced ML research in electronics and embedded systems.

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Each lab folder contains:

• 📄 Problem statement (PDF or DOCX)
• ✍️ Hand calculations / derivations (scanned or LaTeX notes)
• 💻 Python code (.py or Jupyter notebooks)
• 📊 Results & plots

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🛠 Tools & Libraries

• Python 3.x
• NumPy, SciPy
• Matplotlib / Seaborn
• scikit-learn (only where allowed; some labs require manual implementation)

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📖 Reference Texts

• Deep Learning – Ian Goodfellow, Yoshua Bengio, Aaron Courville
• Pattern Recognition and Machine Learning – Christopher M. Bishop
• An Introduction to Statistical Learning – James, Witten, Hastie, Tibshirani

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✨ Acknowledgements

BITS Pilani Hyderabad Campus
EEE G513 – Machine Learning for Electronics Engineers
Instructor: Prof. R. Venkateswaran