Rajiv Gandhi Proudyogiki Vishwavidyalaya (RGPV) B.Tech Machine Learning Notes PDFs for Computer Science Engineering (CSE)

Machine Learning Definition
Machine learning is the application of algorithms to allow computers to learn from data and make predictions or decisions without being explicitly programmed. It enables computers to improve their performance over time by learning from their mistakes.

Data Analysis and Prediction Role
Machine learning is critical in analysing large datasets and extracting useful insights. It’s also used to create predictive models that help forecasters make accurate predictions based on historical data.

Importance of Machine Learning

Improving Decision-Making
Machine learning provides engineers with the tools they need to make sound decisions by analysing patterns and trends in data. This is especially useful in fields where data-driven decisions are crucial.

Automating and Improving Efficiency
Automation is a defining feature of modern engineering. Machine learning automates data-intensive and repetitive tasks, allowing engineers to concentrate on more complex and creative aspects of their work.

Promoting Creativity and Problem Solving
Machine learning opens up new possibilities by allowing engineers to solve complex problems that were previously difficult, if not impossible, to solve using traditional methods.

Driving Technology Personalization
Machine learning enables personalised experiences in a wide range of applications, from recommendation systems to adaptive user interfaces, increasing user satisfaction and engagement.

Course Objectives:

Machine Learning Foundations
Students begin by learning about the fundamental concepts of machine learning, such as learning types, model training, and evaluation.

Reinforcement Learning, Supervised Learning, and Unsupervised Learning
The curriculum covers the following machine learning categories: supervised learning (classification and regression), unsupervised learning (clustering and dimensionality reduction), and reinforcement learning (reward-based learning).

Model Evaluation and Feature Engineering
Students learn how to select and engineer relevant features from data, as well as how to use metrics and validation techniques to assess the performance of machine learning models.

RGPV B.Tech Computer Science Engineering (CSE) Machine Learning (CS601) Notes