Introduction to Pattern Recognition & Machine Learning | Winter 2024

Comprehensive course covering fundamental machine learning concepts, algorithms, and real-world applications. Hands-on experience with Python and popular libraries.

Introduction

This course provides an introduction to pattern recognition and machine learning, covering fundamental concepts, algorithms, and applications. Students will learn about supervised and unsupervised learning techniques, feature extraction, and model evaluation.

Highlights

• Covers a wide range of machine learning algorithms, including linear regression, logistic regression, decision trees, and neural networks
• Hands-on experience with implementing and applying machine learning models using Python and popular libraries like scikit-learn
• Emphasis on practical applications and real-world case studies
• Opportunity to work on a final project to apply course concepts to a problem of the student's choice

Recommendation

This course is recommended for students interested in data science, artificial intelligence, and the practical application of machine learning. It provides a solid foundation for further study in machine learning and is suitable for both beginners and those with some prior experience in the field.

YouTube Videos