This course can be taken for academic credit as part of CU Boulder’s Master of Science in Data Science (MS-DS) degree offered on the Coursera platform. The MS-DS is an interdisciplinary degree that brings together faculty from CU Boulder’s departments of Applied Mathematics, Computer Science, Information Science, and others. With performance-based admissions and no application process, the MS-DS is ideal for individuals with a broad range of undergraduate education and/or professional experience in computer science, information science, mathematics, and statistics. Learn more about the MS-DS program at https://www.coursera.org/degrees/master-of-science-data-science-boulder.
Deep Learning Applications for Computer Vision
科罗拉多大学波德分校课程信息
Basic calculus (differentiation and integration), linear algebra
您将学到的内容有
Learners will be able to explain what Computer Vision is and give examples of Computer Vision tasks.
Learners will be able to describe the process behind classic algorithmic solutions to Computer Vision tasks and explain their pros and cons.
Learners will be able to use hands-on modern machine learning tools and python libraries.
您将获得的技能
- Computer Vision
- Convolutional Neural Network
- Machine Learning
- Deep Learning
Basic calculus (differentiation and integration), linear algebra
提供方

科罗拉多大学波德分校
CU-Boulder is a dynamic community of scholars and learners on one of the most spectacular college campuses in the country. As one of 34 U.S. public institutions in the prestigious Association of American Universities (AAU), we have a proud tradition of academic excellence, with five Nobel laureates and more than 50 members of prestigious academic academies.
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授课大纲 - 您将从这门课程中学到什么
Introduction and Background
In this module, you will learn about the field of Computer Vision. Computer Vision has the goal of extracting information from images. We will go over the major categories of tasks of Computer Vision and we will give examples of applications from each category. With the adoption of Machine Learning and Deep Learning techniques, we will look at how this has impacted the field of Computer Vision.
Classic Computer Vision Tools
In this module, you will learn about classic Computer Vision tools and techniques. We will explore the convolution operation, linear filters, and algorithms for detecting image features.
Image Classification in Computer Vision
In this module we will first review the challenges for object recognition in Classic Computer Vision. Then we will go through the steps of achieving object recognition and image classification in the Classic Computer Vision pipeline.
Neural Networks and Deep Learning
In this module we will compare how the image classification pipeline with neural networks differs than the one with classic computer vision tools. Then we will review the basic components of a neural network. We will conclude with a tutorial in Tensor flow where we will practice how to build, train and use a neural network for image classification predictions.
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