Computer Vision: From Fundamentals to Research Frontiers
“An 8-week intensive roadmap for a Physics SME to transition into a Machine Learning Researcher specializing in Computer Vision. This plan covers classical techniques, modern deep learning architectures, and cutting-edge research topics, emphasizing mathematical intuition and practical implementation.”
Week 1: Foundations of Image Processing & Classical Vision
By the end of this module you will be able to implement fundamental image manipulation and feature extraction algorithms from first principles.
Week 2: Deep Learning Foundations & Convolutional Neural Networks
By the end of this module you will be able to build, train, and debug a Convolutional Neural Network (CNN) from scratch for an image classification task.
Week 3: Modern CNN Architectures & Transfer Learning
By the end of this module you will be able to leverage state-of-the-art pre-trained models to solve custom image classification problems efficiently.
Week 4: Object Detection and Semantic Segmentation
By the end of this module you will be able to implement and train models for localizing and classifying multiple objects within an image.
Week 5: Generative Models in Vision
By the end of this module you will be able to understand the theory behind and implement deep generative models to synthesize novel images.
Week 6: Transformers in Vision
By the end of this module you will be able to adapt and apply Transformer-based architectures to computer vision tasks.
Week 7: 3D Vision and Multi-Modal Learning
By the end of this module you will be able to understand and experiment with models that reason about 3D scenes and connect vision with other modalities like text.
Week 8: Research Frontiers & Project Proposal
By the end of this module you will be able to identify a research gap in computer vision and formulate a structured proposal to address it.
Learn
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Practice
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