4.5
stars
45 ratings

Ioana Fleming

3,675 already enrolled

Offered By

Part of the Master of Science in Data Science degree
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Deep Learning Applications for Computer Vision

University of Colorado Boulder

About this Course

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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.
In this course, you’ll be learning about Computer Vision as a field of study and research. First we’ll be exploring several Computer Vision tasks and suggested approaches, from the classic Computer Vision perspective. Then we’ll introduce Deep Learning methods and apply them to some of the same problems. We will analyze the results and discuss advantages and drawbacks of both types of methods. We'll use tutorials to let you explore hands-on some of the modern machine learning tools and software libraries. Examples of Computer Vision tasks where Deep Learning can be applied include: image classification, image classification with localization, object detection, object segmentation, facial recognition, and activity or pose estimation.

Flexible deadlines

Reset deadlines in accordance to your schedule.

Shareable Certificate

Earn a Certificate upon completion

100% online

Start instantly and learn at your own schedule.

Intermediate Level

Basic calculus (differentiation and integration), linear algebra

Approx. 22 hours to complete

English

What you will learn

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.

Skills you will gain

Computer Vision
Convolutional Neural Network
Machine Learning
Deep Learning

Flexible deadlines

Reset deadlines in accordance to your schedule.

Shareable Certificate

Earn a Certificate upon completion

100% online

Start instantly and learn at your own schedule.

Intermediate Level

Basic calculus (differentiation and integration), linear algebra

Approx. 22 hours to complete

English

Instructor

Instructor rating

4.83/5 (13 Ratings)

Ioana Fleming

Associate Teaching Professor and Chair of Undergraduate Education

Computer Science

3,693 Learners

2 Courses

Offered by

University of Colorado Boulder

Start working towards your Master's degree

This course is part of the 100% online Master of Science in Data Science from University of Colorado Boulder. If you are admitted to the full program, your courses count towards your degree learning.

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Syllabus - What you will learn from this course

Week1

Week 1

4 hours to complete

Introduction and Background

4 hours to complete

4 videos (Total 80 min), 12 readings, 1 quiz

4 videos

Lecture 121mLecture 217mLecture 319mLecture 421m

12 readings

Welcome and Where to Find Help 10mInformation on Peer Reviews10mWhat is Computer Vision?10mLecture 1 notes10mLecture 2 notes10mReadings and Resources10mTED Talk: "How We're Teaching Computers to Understand Pictures" Prof. Fei-Fei Li10mLecture 3 notes10mReadings and Resources10mEthics of Driverless Cars - The New Yorker Magazine10mLecture 4 notes10mReadings and Resources10m

1 practice exercise

Computer Vision Areas and Applications30m

Week2

Week 2

4 hours to complete

Classic Computer Vision Tools

4 hours to complete

5 videos (Total 115 min), 10 readings, 1 quiz

Week3

Week 3

3 hours to complete

Image Classification in Computer Vision

3 hours to complete

3 videos (Total 45 min), 2 readings, 1 quiz

Week4

Week 4

5 hours to complete

Neural Networks and Deep Learning

5 hours to complete

4 videos (Total 100 min), 5 readings, 1 quiz

Reviews

4.5

9 reviews

5 stars
73.33%
4 stars
17.77%
3 stars
4.44%
1 star
4.44%

TOP REVIEWS FROM DEEP LEARNING APPLICATIONS FOR COMPUTER VISION

by JPJan 2, 2022

Great introductory course on deep learning for computer vision.

by DMJun 16, 2022

Learnt many things and most exciting was Python code part

View all reviews

Frequently Asked Questions

When will I have access to the lectures and assignments?
Access to lectures and assignments depends on your type of enrollment. If you take a course in audit mode, you will be able to see most course materials for free. To access graded assignments and to earn a Certificate, you will need to purchase the Certificate experience, during or after your audit. If you don't see the audit option:
The course may not offer an audit option. You can try a Free Trial instead, or apply for Financial Aid.
The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.
What will I get if I purchase the Certificate?
When you purchase a Certificate you get access to all course materials, including graded assignments. Upon completing the course, your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile. If you only want to read and view the course content, you can audit the course for free.
Is financial aid available?
Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.

More questions? Visit the Learner Help Center.

Deep Learning Applications for Computer Vision

Deep Learning Applications for Computer Vision

About this Course

What you will learn

Skills you will gain