Pattern Recognition Notes

Welcome to my pattern recognition learning notes! In this document, I will be sharing my insights, observations, and understanding of pattern recognition techniques and algorithms. Pattern recognition is a fascinating field that involves the identification and interpretation of patterns in data, enabling us to make predictions, classifications, and decisions based on those patterns.

Throughout these learning notes, I will cover various topics related to pattern recognition, including different algorithms, feature extraction methods, classification techniques, and evaluation metrics. I will also provide examples and practical exercises to help solidify your understanding of these concepts.

On the top of the pages, you will find a small rocket . This is the binder which allows you to interactively run the notebooks in the cloud.

Whether you are a beginner looking to explore the basics of pattern recognition or an experienced practitioner seeking to deepen your knowledge, these learning notes will serve as a valuable resource. So, let’s dive in and embark on this exciting journey of pattern recognition!

Basics

• Formular Estimator
• Multilayer Perceptron
• Task: Classify Iris Flower
• Convolutional Neural Network
• Clustering
• Principial Component Analysis
• Kalman Filter
• PyTorch

Background

• Perceptron
• Optimizers
• Softmax
• Convolution

Advanced

• Recurrent Neural Network
• Reinforcement Learning
• Deep Q Learning
• Object Detection

Reference

• Abbreviations
• Links
• Self Assessment

Computer Science

• P-NP Problems