Computer Vision Master Class - Python Primer Part 1

Computer Vision Master Class Introduction with Python, ML Maths Primers + Core Concepts

This curriculum outlines a comprehensive Computer Vision (CV) Master Class, covering Python programming, Machine Learning (ML) fundamentals, essential mathematics, and core CV concepts.
Part 1: Introduction & Prerequisites

1) Welcome & Course Overview: Introduce the course structure, learning objectives, and career opportunities in CV.
2) Getting Started with Python: Learn Python syntax, data structures, control flow, functions, and libraries like NumPy, Pandas, and Matplotlib. (Hands-on exercises included)
3) Essential Maths for CV: Cover linear algebra (vectors, matrices, transformations), calculus (derivatives, integrals), and probability & statistics (distributions, hypothesis testing). (Interactive tutorials and practical examples)

Part 2: Machine Learning Fundamentals

1) Supervised Learning: Introduction to supervised learning concepts, classification ; regression algorithms, and evaluation metrics.
2) Unsupervised Learning: Explore unsupervised learning techniques like clustering, dimensionality reduction, and anomaly detection.
3) Deep Learning for CV: Understand Convolutional Neural Networks (CNNs) architecture, backpropagation, and training deep learning models. (Implement a simple CNN for image classification)

Part 3: Core Computer Vision Concepts

1) Image Processing: Learn fundamental image processing techniques like filtering, image manipulation, color spaces, and edge detection. (Develop image processing pipelines in Python)
2) Feature Extraction: Explore feature extraction methods for images, including SIFT, SURF, ORB, and histograms. (Implement feature extraction algorithms)
3) Object Detection & Recognition: Discover object detection & recognition techniques like YOLO, R-CNN, and object classification using CNNs. (Train your own object detection model)
4) Image Segmentation: Learn image segmentation techniques like thresholding, clustering, and semantic segmentation with deep learning. (Implement image segmentation algorithms)
5) Motion Analysis: Explore optical flow, background subtraction, and video analysis techniques with applications like object tracking. (Develop basic motion analysis tools)
6) Applications of CV: Discuss real-world applications of CV in areas like autonomous vehicles, medical imaging, robotics, and security systems.

Part 4: Advanced Topics (Optional)

1) Generative Adversarial Networks (GANs): Explore GAN architecture, training, and applications in image generation and manipulation.
2) 3D Computer Vision: Learn about depth perception, point cloud processing, and 3D object recognition techniques.
3) Computer Vision for Robotics: Explore how CV empowers robots to perceive their environment, navigate, and interact with objects.

Resources & Learning Materials:

1) Online lectures, video tutorials, interactive coding notebooks.
2) Code repositories containing implementations of CV algorithms.
3) Datasets for training and evaluating CV models.
4) Project ideas to apply the learned concepts to real-world problems.

Assessment:

1) Quizzes and coding exercises after each module to solidify understanding.
2) Final project where you apply your learnings to build a complete CV application.

Benefits of this Master Class:

1) Gain a strong foundation in Python, ML, and mathematics for CV.
2) Master fundamental and advanced CV concepts through hands-on practice.
3) Build your own computer vision applications with Python libraries.
4) Prepare for a career in computer vision or related fields.

Target Audience:

1) Beginners with an interest in computer vision and artificial intelligence.
2) Developers and programmers who want to expand their skill set.
3) Data scientists and researchers seeking to apply CV techniques in their work.