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This course immerses learners in deep learning, preparing them to solve computer vision problems. Learners plunge into the field of computer vision that deals with recognizing, identifying and understanding visual information from visual data, whether the information is from a single image or video sequence. Topics include object detection, face detection and recognition (using Adaboost and Eigenfaces), and the progression of deep learning techniques (CNN, AlexNet, REsNet, and Generative Models.)
This course is ideal for anyone curious about or interested in exploring the concepts of visual recognition and deep learning...
Read more
This course immerses learners in deep learning, preparing them to solve computer vision problems. Learners plunge into the field of computer vision that deals with recognizing, identifying and understanding visual information from visual data, whether the information is from a single image or video sequence. Topics include object detection, face detection and recognition (using Adaboost and Eigenfaces), and the progression of deep learning techniques (CNN, AlexNet, REsNet, and Generative Models.)
This course is ideal for anyone curious about or interested in exploring the concepts of visual recognition and deep learning...
Read more
This course immerses learners in deep learning, preparing them to solve computer vision problems. Learners plunge into the field of computer vision that deals with recognizing, identifying and understanding visual information from visual data, whether the information is from a single image or video sequence. Topics include object detection, face detection and recognition (using Adaboost and Eigenfaces), and the progression of deep learning techniques (CNN, AlexNet, REsNet, and Generative Models.)
This course is ideal for anyone curious about or interested in exploring the concepts of visual recognition and deep learning computer vision. Learners should have basic programming skills and experience (understanding of for loops, if/else statements), specifically in MATLAB (free introductory tutorial: https://www.mathworks.com/learn/tutorials/matlab-onramp.html). Learners should also be familiar with the following: basic linear algebra (matrix vector operations and notation), 3D co-ordinate systems and transformations, basic calculus (derivatives and integration) and basic probability (random variables). It is highly recommended that learners take the “Deep Learning Onramp” course available at https://matlabacademy.mathworks.com/.
Material includes online lectures, videos, demos, hands-on exercises, project work, readings and discussions. Learners gain experience writing computer vision programs through online labs using MATLAB* and supporting toolboxes.
This is the fourth course in the Computer Vision specialization that lays the groundwork necessary for designing sophisticated vision applications. To learn more about the specialization, check out a video overview at https://youtu.be/OfxVUSCPXd0.
* A free license to install MATLAB for the duration of the course is available from MathWorks.
Register for this course and see more details by visiting:
OpenCourser.com/course/uwj6gk/visual
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Covers fundamentals in visual recognition and develops foundational skills in computer vision
Teaches advanced computer vision techniques and models, industry-standard in computer vision
Provides hands-on experience with computer vision programs through online labs, leveraging MATLAB's strengths for visual processing
Assumes familiarity with basic programming, linear algebra, calculus, and probability, suitable for learners with a technical background
Requires MATLAB installation, which may not be readily available to all learners
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Reviews summary
Visual understanding and recognition
This course offers an immersive introduction to computer vision and deep learning. The curriculum includes object, face, emotion, and place detection and recognition. Students write computer vision programs using MATLAB and toolboxes. While code-heavy, one student noted that the first test answer was not formal enough.
Hands-on exercises and labs use MATLAB.
"Learners gain experience writing computer vision programs through online labs using MATLAB* and supporting toolboxes."
Immerses learners in the fundamentals of computer vision.
"This course immerses learners in deep learning, preparing them to solve computer vision problems."
Covers concepts and techniques in deep learning.
"Learners plunge into the field of computer vision that deals with recognizing, identifying and understanding visual information from visual data, whether the information is from a single image or video sequence."
Requires basic programming skills, linear algebra, calculus, and probability.
"Learners should have basic programming skills and experience (understanding of for loops, if/else statements), specifically in MATLAB (free introductory tutorial: https://www.mathworks.com/learn/tutorials/matlab-onramp.html)."
Activities
Be better prepared
before
your course. Deepen your understanding
during
and
after
it. Supplement your coursework and achieve mastery of the topics covered
in Visual Recognition & Understanding with these
activities:
Calculus Review
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Review essential calculus concepts, including derivatives and integration, to strengthen your mathematical foundation and enhance your understanding of Deep Learning.
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Calculus
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Review of linear algebra
Show steps
Revisit the concepts of linear algebra, including matrix and vector operations, to strengthen your foundation and enhance your understanding of Deep Learning.
Browse courses on
Matrix Operations
Show steps
-
Review basic matrix and vector operations.
-
Solve systems of linear equations using methods like Gaussian elimination.
-
Explore concepts such as matrix multiplication and determinants.
Compile Course Notes
Show steps
Organize and review your course materials to strengthen your foundational understanding.
Show steps
-
Gather all course notes, assignments, quizzes, and exams.
-
Review the materials and identify key concepts and ideas.
-
Organize the materials into a coherent and logical structure.
-
Review the compiled materials regularly to reinforce your understanding.
13 other activities
Expand to see all activities and additional details
Show all 16 activities
MATLAB Tutorials
Show steps
Follow tutorials to reinforce and enhance your MATLAB skills, strengthening your ability to apply them in computer vision projects.
Browse courses on
MATLAB
Show steps
-
Identify specific areas where you want to improve your MATLAB skills.
-
Search for tutorials that cover those areas.
-
Follow the tutorials, completing the exercises and examples.
Discussion Forums
Show steps
Engage in discussions with peers to clarify concepts, exchange ideas, and reinforce your understanding of the course material.
Browse courses on
Computer Vision
Show steps
-
Identify relevant discussion forums related to computer vision or deep learning.
-
Participate actively in discussions, asking questions and sharing your insights.
-
Reflect on the discussions and how they contribute to your learning.
Collaborative Problem Solving
Show steps
Engage in collaborative problem-solving sessions with peers to tackle challenging concepts and gain diverse perspectives.
Browse courses on
Problem Solving
Show steps
-
Form study groups or join online forums.
-
Discuss complex topics, share ideas, and work together to find solutions.
Deep Learning with Python
Show steps
Read a book that provides a comprehensive overview of deep learning concepts and techniques, expanding your knowledge.
View
Deep Learning with Python, Second Edition
on Amazon
Show steps
-
Read the book and take notes on key concepts.
-
Complete the exercises and examples provided in the book.
-
Reflect on the material and connect it to what you're learning in the course.
Object Detection Practice
Show steps
Engage in hands-on practice with object detection algorithms to improve your understanding of their implementation and enhance your programming skills.
Browse courses on
Object Detection
Show steps
-
Implement a simple object detection algorithm using MATLAB.
-
Evaluate the performance of your algorithm on various datasets.
Object Detection Exercises
Show steps
Practice object detection exercises to develop your ability to identify and locate objects in images, enhancing your understanding of computer vision algorithms.
Browse courses on
Object Detection
Show steps
-
Find a dataset of images with labeled objects.
-
Write a program that detects objects in the images.
-
Evaluate the performance of your program.
CNNs and Deep Learning
Show steps
Delve deeper into the world of Convolutional Neural Networks and Deep Learning through guided tutorials, solidifying your understanding of their architectures and applications in visual recognition.
Browse courses on
Convolutional Neural Networks
Show steps
-
Explore the fundamentals of CNNs and their layers.
-
Build and train a CNN for image classification using popular frameworks.
-
Visualize the features learned by CNNs through techniques like Grad-CAM.
Create a Computer Vision Project
Show steps
Create a project to apply the concepts and techniques learned in the course, reinforcing your understanding and developing practical skills.
Browse courses on
Computer Vision
Show steps
-
Identify a project idea that aligns with your interests and learning goals.
-
Gather data and resources to support your project.
-
Develop an algorithm or model for your project.
-
Implement your algorithm or model in MATLAB.
-
Evaluate the performance of your project and iterate to improve results.
Visualizing Data
Show steps
Create compelling visualizations to present your findings from computer vision experiments, enhancing your communication and storytelling skills.
Browse courses on
Data Visualization
Show steps
-
Use Python libraries like Matplotlib and Seaborn to generate charts and graphs.
-
Explore advanced techniques like interactive dashboards and 3D visualizations.
-
Develop a strong visual vocabulary to effectively convey data insights.
Image Classification Project
Show steps
Build an image classification project to gain hands-on experience in training and evaluating deep learning models.
Browse courses on
Image Classification
Show steps
-
Gather a dataset of labeled images.
-
Preprocess the images and prepare them for training.
-
Design and train a deep learning model for image classification.
-
Evaluate the performance of your model and fine-tune it to improve accuracy.
-
Deploy your model and use it to classify new images.
Contributing to Open-Source Computer Vision Projects
Show steps
Immerse yourself in the open-source community by contributing to projects related to computer vision, expanding your knowledge and networking with experts.
Browse courses on
Open Source
Show steps
-
Identify open-source computer vision projects seeking contributions.
-
Contribute to bug fixes, feature development, or documentation.
Presentation on a Computer Vision Topic
Show steps
Create a presentation to demonstrate your understanding of a specific computer vision topic, reinforcing your knowledge and communication skills.
Browse courses on
Computer Vision
Show steps
-
Choose a specific computer vision topic that interests you.
-
Research the topic and gather relevant information.
-
Develop a presentation that clearly explains the topic, including examples and visuals.
-
Practice your presentation and deliver it to an audience.
-
Reflect on your presentation and identify areas for improvement.
Face Recognition Project
Show steps
Embark on a hands-on project to build a face recognition system, putting your knowledge of image processing and machine learning into practice.
Browse courses on
Face Recognition
Show steps
-
Collect and preprocess a dataset of facial images.
-
Implement face detection and feature extraction algorithms.
-
Develop a recognition model using techniques like Eigenfaces or Fisherfaces.
Calculus Review
Show steps
Review essential calculus concepts, including derivatives and integration, to strengthen your mathematical foundation and enhance your understanding of Deep Learning.
Browse courses on
Calculus
Show steps
Show steps
Show steps
Review of linear algebra
Show steps
Revisit the concepts of linear algebra, including matrix and vector operations, to strengthen your foundation and enhance your understanding of Deep Learning.
Browse courses on
Matrix Operations
Show steps
Show steps
Show steps
- Review basic matrix and vector operations.
- Solve systems of linear equations using methods like Gaussian elimination.
- Explore concepts such as matrix multiplication and determinants.
Compile Course Notes
Show steps
Organize and review your course materials to strengthen your foundational understanding.
Show steps
Show steps
Show steps
- Gather all course notes, assignments, quizzes, and exams.
- Review the materials and identify key concepts and ideas.
- Organize the materials into a coherent and logical structure.
- Review the compiled materials regularly to reinforce your understanding.
13 other activities
Expand to see all activities and additional details
Show all 16 activities
MATLAB Tutorials
Show steps
Follow tutorials to reinforce and enhance your MATLAB skills, strengthening your ability to apply them in computer vision projects.
Browse courses on
MATLAB
Show steps
Show steps
Show steps
- Identify specific areas where you want to improve your MATLAB skills.
- Search for tutorials that cover those areas.
- Follow the tutorials, completing the exercises and examples.
Discussion Forums
Show steps
Engage in discussions with peers to clarify concepts, exchange ideas, and reinforce your understanding of the course material.
Browse courses on
Computer Vision
Show steps
Show steps
Show steps
- Identify relevant discussion forums related to computer vision or deep learning.
- Participate actively in discussions, asking questions and sharing your insights.
- Reflect on the discussions and how they contribute to your learning.
Collaborative Problem Solving
Show steps
Engage in collaborative problem-solving sessions with peers to tackle challenging concepts and gain diverse perspectives.
Browse courses on
Problem Solving
Show steps
Show steps
Show steps
- Form study groups or join online forums.
- Discuss complex topics, share ideas, and work together to find solutions.
Deep Learning with Python
Show steps
Read a book that provides a comprehensive overview of deep learning concepts and techniques, expanding your knowledge.
View
Deep Learning with Python, Second Edition
on Amazon
Show steps
Show steps
Show steps
- Read the book and take notes on key concepts.
- Complete the exercises and examples provided in the book.
- Reflect on the material and connect it to what you're learning in the course.
Object Detection Practice
Show steps
Engage in hands-on practice with object detection algorithms to improve your understanding of their implementation and enhance your programming skills.
Browse courses on
Object Detection
Show steps
Show steps
Show steps
- Implement a simple object detection algorithm using MATLAB.
- Evaluate the performance of your algorithm on various datasets.
Object Detection Exercises
Show steps
Practice object detection exercises to develop your ability to identify and locate objects in images, enhancing your understanding of computer vision algorithms.
Browse courses on
Object Detection
Show steps
Show steps
Show steps
- Find a dataset of images with labeled objects.
- Write a program that detects objects in the images.
- Evaluate the performance of your program.
CNNs and Deep Learning
Show steps
Delve deeper into the world of Convolutional Neural Networks and Deep Learning through guided tutorials, solidifying your understanding of their architectures and applications in visual recognition.
Browse courses on
Convolutional Neural Networks
Show steps
Show steps
Show steps
- Explore the fundamentals of CNNs and their layers.
- Build and train a CNN for image classification using popular frameworks.
- Visualize the features learned by CNNs through techniques like Grad-CAM.
Create a Computer Vision Project
Show steps
Create a project to apply the concepts and techniques learned in the course, reinforcing your understanding and developing practical skills.
Browse courses on
Computer Vision
Show steps
Show steps
Show steps
- Identify a project idea that aligns with your interests and learning goals.
- Gather data and resources to support your project.
- Develop an algorithm or model for your project.
- Implement your algorithm or model in MATLAB.
- Evaluate the performance of your project and iterate to improve results.
Visualizing Data
Show steps
Create compelling visualizations to present your findings from computer vision experiments, enhancing your communication and storytelling skills.
Browse courses on
Data Visualization
Show steps
Show steps
Show steps
- Use Python libraries like Matplotlib and Seaborn to generate charts and graphs.
- Explore advanced techniques like interactive dashboards and 3D visualizations.
- Develop a strong visual vocabulary to effectively convey data insights.
Image Classification Project
Show steps
Build an image classification project to gain hands-on experience in training and evaluating deep learning models.
Browse courses on
Image Classification
Show steps
Show steps
Show steps
- Gather a dataset of labeled images.
- Preprocess the images and prepare them for training.
- Design and train a deep learning model for image classification.
- Evaluate the performance of your model and fine-tune it to improve accuracy.
- Deploy your model and use it to classify new images.
Contributing to Open-Source Computer Vision Projects
Show steps
Immerse yourself in the open-source community by contributing to projects related to computer vision, expanding your knowledge and networking with experts.
Browse courses on
Open Source
Show steps
Show steps
Show steps
- Identify open-source computer vision projects seeking contributions.
- Contribute to bug fixes, feature development, or documentation.
Presentation on a Computer Vision Topic
Show steps
Create a presentation to demonstrate your understanding of a specific computer vision topic, reinforcing your knowledge and communication skills.
Browse courses on
Computer Vision
Show steps
Show steps
Show steps
- Choose a specific computer vision topic that interests you.
- Research the topic and gather relevant information.
- Develop a presentation that clearly explains the topic, including examples and visuals.
- Practice your presentation and deliver it to an audience.
- Reflect on your presentation and identify areas for improvement.
Face Recognition Project
Show steps
Embark on a hands-on project to build a face recognition system, putting your knowledge of image processing and machine learning into practice.
Browse courses on
Face Recognition
Show steps
Show steps
Show steps
- Collect and preprocess a dataset of facial images.
- Implement face detection and feature extraction algorithms.
- Develop a recognition model using techniques like Eigenfaces or Fisherfaces.
Career center
Learners who complete Visual Recognition & Understanding will develop knowledge and skills
that may be useful to these careers:
Computer Vision Engineer
Computer Vision Engineer
The field of computer vision is ever-growing and those who work in it help computers to see and understand the world around them. Computer vision engineers use their knowledge of artificial intelligence, machine learning, and deep learning to create systems that can identify objects, track movement, and even make decisions based on visual information. With a course like Visual Recognition and Understanding, learners are well-equipped to enter this exciting field. This course provides learners with the skills necessary to build computer vision applications that can solve real-world problems.
Machine Learning Engineer
Machine Learning Engineer
Machine learning engineers are responsible for designing, building, and deploying machine learning models. They use their knowledge of mathematics, statistics, and computer science to create models that can learn from data and make predictions. Visual Recognition and Understanding is a great foundation for those who want to become machine learning engineers. This course provides learners with the skills necessary to build machine learning models that can solve computer vision problems.
Data Scientist
Data Scientist
Data scientists use their knowledge of mathematics, statistics, and computer science to extract insights from data. They use these insights to solve business problems and make better decisions. Visual Recognition and Understanding is a valuable course for data scientists who want to work with visual data. This course provides learners with the skills necessary to extract insights from visual data and use these insights to solve business problems.
Software Engineer
Software Engineer
Software engineers design, develop, and maintain software applications. They use their knowledge of programming languages and software development methodologies to create software that meets the needs of users. Visual Recognition and Understanding is a valuable course for software engineers who want to work with computer vision applications. This course provides learners with the skills necessary to develop computer vision applications that can solve real-world problems.
Computer Graphics Artist
Computer Graphics Artist
Computer graphics artists use their knowledge of computer graphics software and techniques to create visual content for movies, video games, and other media. Visual Recognition and Understanding is a valuable course for computer graphics artists who want to create realistic and believable visual content. This course provides learners with the skills necessary to understand how computers see and understand the world around them, which can help them create more realistic and believable visual content.
Robotics Engineer
Robotics Engineer
Robotics engineers design, build, and maintain robots. They use their knowledge of mechanical engineering, electrical engineering, and computer science to create robots that can perform a variety of tasks. Visual Recognition and Understanding is a valuable course for robotics engineers who want to design and build robots that can see and understand the world around them. This course provides learners with the skills necessary to develop computer vision systems that can be used in robots.
Artificial Intelligence Researcher
Artificial Intelligence Researcher
Artificial intelligence researchers develop new artificial intelligence algorithms and techniques. They use their knowledge of mathematics, statistics, and computer science to create AI systems that can perform a variety of tasks, including computer vision. Visual Recognition and Understanding is a valuable course for artificial intelligence researchers who want to work on computer vision problems. This course provides learners with the skills necessary to develop new computer vision algorithms and techniques.
Computer Vision Scientist
Computer Vision Scientist
Computer vision scientists use their knowledge of computer vision algorithms and techniques to solve real-world problems. They work in a variety of fields, including robotics, medical imaging, and surveillance. Visual Recognition and Understanding is a valuable course for computer vision scientists who want to solve real-world problems using computer vision. This course provides learners with the skills necessary to develop computer vision systems that can be used to solve a variety of problems.
Data Analyst
Data Analyst
Data analysts use their knowledge of mathematics, statistics, and computer science to analyze data and extract insights from it. They use these insights to solve business problems and make better decisions. Visual Recognition and Understanding is a valuable course for data analysts who want to work with visual data. This course provides learners with the skills necessary to extract insights from visual data and use these insights to solve business problems.
Game Developer
Game Developer
Game developers design, develop, and maintain video games. They use their knowledge of programming languages, game engines, and game design principles to create games that are fun and engaging. Visual Recognition and Understanding is a valuable course for game developers who want to create games that feature realistic and believable computer vision. This course provides learners with the skills necessary to develop computer vision systems that can be used in games.
User Experience Designer
User Experience Designer
User experience designers design and develop user interfaces for websites, mobile apps, and other software products. They use their knowledge of human-computer interaction and user experience design principles to create user interfaces that are easy to use and enjoyable. Visual Recognition and Understanding is a valuable course for user experience designers who want to design user interfaces that feature computer vision. This course provides learners with the skills necessary to understand how computers see and understand the world around them, which can help them design user interfaces that are more natural and intuitive.
Web Developer
Web Developer
Web developers design, develop, and maintain websites. They use their knowledge of programming languages and web development frameworks to create websites that are informative, engaging, and easy to use. Visual Recognition and Understanding is a valuable course for web developers who want to create websites that feature computer vision. This course provides learners with the skills necessary to develop computer vision systems that can be used on websites.
Mobile App Developer
Mobile App Developer
Mobile app developers design, develop, and maintain mobile apps. They use their knowledge of programming languages and mobile app development frameworks to create mobile apps that are useful, engaging, and easy to use. Visual Recognition and Understanding is a valuable course for mobile app developers who want to create mobile apps that feature computer vision. This course provides learners with the skills necessary to develop computer vision systems that can be used in mobile apps.
Photographer
Photographer
Photographers use their knowledge of photography techniques and equipment to capture images. They use these images to tell stories, document events, and create works of art. Visual Recognition and Understanding is a valuable course for photographers who want to understand how computers see and interpret images. This course provides learners with the skills necessary to use computer vision to improve their photography skills.
Graphic designer
Graphic designer
Graphic designers use their knowledge of design principles and software to create visual content for a variety of purposes, including marketing, advertising, and web design. Visual Recognition and Understanding is a valuable course for graphic designers who want to understand how computers see and interpret images. This course provides learners with the skills necessary to use computer vision to improve their graphic design skills.
For more career information including salaries, visit:
OpenCourser.com/course/uwj6gk/visual
Reading list
We've selected nine books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
Visual Recognition & Understanding.
Provides a comprehensive introduction to probabilistic graphical models. It covers the basics of probabilistic graphical models, as well as more advanced topics such as Bayesian networks, Markov random fields, and conditional random fields.
Provides a practical introduction to deep learning for computer vision. It covers the basics of deep learning, as well as more advanced topics such as convolutional neural networks, recurrent neural networks, and generative adversarial networks.
Provides a practical introduction to deep learning using Python. It covers the basics of deep learning, as well as more advanced topics such as convolutional neural networks, recurrent neural networks, and generative adversarial networks.
Comprehensive introduction to computer vision. It covers the fundamental concepts of computer vision, as well as more advanced topics such as object recognition, image segmentation, and motion analysis.
Provides a practical introduction to TensorFlow, a popular open-source deep learning library. It covers the basics of TensorFlow, as well as more advanced topics such as convolutional neural networks, recurrent neural networks, and generative adversarial networks.
Provides a practical introduction to deep learning using Python. It covers the basics of deep learning, as well as more advanced topics such as convolutional neural networks, recurrent neural networks, and generative adversarial networks.
Save
Provides a practical introduction to deep learning using MATLAB. It covers the basics of deep learning, as well as more advanced topics such as convolutional neural networks, recurrent neural networks, and generative adversarial networks.
Save
Covers the fundamental concepts of computer vision, including image formation, feature detection, image segmentation, object recognition, and motion analysis. It comprehensive resource for students and researchers in the field of computer vision.
Provides a practical introduction to OpenCV using Python. It covers the basics of OpenCV, as well as more advanced topics such as object detection, image segmentation, and machine learning for computer vision.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/uwj6gk/visual
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Time
20 hours
Via
Coursera
Institutions
The State University of New York
University at Buffalo
This course belongs to a
collection
called
Computer Vision . It's comprised of four courses:
- Image Processing, Features & Segmentation
- Computer Vision Basics
- Visual Recognition & Understanding (viewing)
- Stereo Vision, Dense Motion & Tracking
Good to know
Covers fundamentals in visual recognition and develops foundational skills in computer vision
Teaches advanced computer vision techniques and models, industry-standard in computer vision
Provides hands-on experience with computer vision programs through online labs, leveraging MATLAB's strengths for visual processing
Assumes familiarity with basic programming, linear algebra, calculus, and probability, suitable for learners with a technical background
Requires MATLAB installation, which may not be readily available to all learners
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