Computer Vision

University of Colorado Boulder via Coursera Specialization

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The Computer Vision specialization takes you from the foundations of computer vision to the cutting edge of multimodal AI. Whether you're just starting out or looking to deepen your expertise, you'll gain the skills to build intelligent systems that interpret and generate visual data—just like today’s most advanced AI models.

Syllabus

Course 1: Introduction to Computer Vision
Course 2: Deep Learning for Computer Vision
Course 3: Modern AI Models for Vision and Multimodal Understanding

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Introduction to Computer Vision guides learners through the essential algorithms and methods to help computers 'see' and interpret visual data. You will first learn the core concepts and techniques that have been traditionally used to analyze images. Then, you will learn modern deep learning methods, such as neural networks and specific models designed for image recognition, and how it can be used to perform more complex tasks like object detection and image segmentation. Additionally, you will learn the creation and impact of AI-generated images and videos, exploring the ethical considerations of such technology. This course can be taken for academic credit as part of CU Boulder’s MS in Data Science or MS in Computer Science degrees offered on the Coursera platform. These fully accredited graduate degrees offer targeted courses, short 8-week sessions, and pay-as-you-go tuition. Admission is based on performance in three preliminary courses, not academic history. CU degrees on Coursera are ideal for recent graduates or working professionals. Learn more: MS in Data Science: https://www.coursera.org/degrees/master-of-science-data-science-boulder MS in Computer Science: https://coursera.org/degrees/ms-computer-science-boulder
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Unlock the power of deep learning to transform visual data into actionable insights. This hands-on course guides you through the foundational and advanced techniques that drive modern computer vision applications—from image classification to generative modeling. You'll begin with the building blocks of deep learning - understanding how multilayer perceptrons (MLPs) work, and exploring normalization techniques that stabilize and accelerate training. You'll then dive into unsupervised learning with autoencoders and discover the magic behind Generative Adversarial Networks (GANs) that can create realistic images from noise. After, you'll master the architecture that revolutionized computer vision by learning how CNNs extract spatial hierarchies and patterns from images for tasks like object detection and recognition. Finally, you'll explore cutting-edge architectures. ResNet introduces residual learning for deeper networks, while U-Net powers precise image segmentation in medical imaging and beyond. Whether you're a data scientist, engineer, or AI enthusiast, this course equips you with the skills to build and deploy deep learning models for real-world vision tasks. With practical examples and guided learning, you'll gain both theoretical understanding and hands-on experience. This course can be taken for academic credit as part of CU Boulder’s MS in Data Science or MS in Computer Science degrees offered on the Coursera platform. These fully accredited graduate degrees offer targeted courses, short 8-week sessions, and pay-as-you-go tuition. Admission is based on performance in three preliminary courses, not academic history. CU degrees on Coursera are ideal for recent graduates or working professionals. Learn more: MS in Data Science: https://www.coursera.org/degrees/master-of-science-data-science-boulder MS in Computer Science: https://coursera.org/degrees/ms-computer-science-boulder
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Step into the frontier of artificial intelligence with this advanced course designed to explore the latest models powering visual and multimodal intelligence. From foundational mathematical tools to state-of-the-art architectures, you'll gain the skills to understand and build systems that interpret images, text, and more—just like today’s leading AI models. You'll begin by discovering how Nonlinear Support Vector Machines (NSVMs) and Fourier transforms lay the groundwork for signal processing and pattern recognition in visual data. You'll then build a strong foundation in probabilistic reasoning and temporal modeling with RNNs, enabling AI systems to understand sequences and context. After, you'll learn how transformer architectures revolutionize both language and vision tasks. Finally, you'll dive into multimodal learning with CLIP, which connects images and text, and explore diffusion models that generate high-fidelity images through iterative refinement. This course is ideal for learners who want to go beyond traditional deep learning and explore the models shaping the future of AI. With a blend of theory, code, and real-world applications, you'll be equipped to tackle cutting-edge challenges in computer vision and multimodal AI. This course can be taken for academic credit as part of CU Boulder’s MS in Data Science or MS in Computer Science degrees offered on the Coursera platform. These fully accredited graduate degrees offer targeted courses, short 8-week sessions, and pay-as-you-go tuition. Admission is based on performance in three preliminary courses, not academic history. CU degrees on Coursera are ideal for recent graduates or working professionals. Learn more: MS in Data Science: https://www.coursera.org/degrees/master-of-science-data-science-boulder MS in Computer Science: https://coursera.org/degrees/ms-computer-science-boulder