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Applied Computer Vision Course

A comprehensive course covering both classical computer vision approaches and ML/DL techniques for computer vision tasks. Boost your domain skills in 2 months!

About the Course

This course on applied computer vision was born after several successful editions of internships and schools held by the It-Jim team. As we are a company that is focused specifically on computer vision and machine learning tasks, our lectures offer the most comprehensive syllabus covering both classical CV algorithms and deep learning approaches with many real project examples as icing on the cake.

Curious how to make computers see and interpret data as humans do? We will guide you from image analysis and processing through multi-view geometry and stereovision up to state-of-the-art neural network architectures. Each lesson is a mixture of theory, enhanced with mathematics essentials for computer vision, and practical workshops showcasing the methods learned.

Online course
  • When
    October 5, 2021
  • How long
    10 weeks / 20 lessons /2h each
  • how many
    20 students in a group
  • how much
    15000 UAH

Course Program

Part 1. Classical computer vision
  • Week 1. Image processing and understanding
    Lecture 1
    Theory
    • What is an image?
    • Image representations
    • Pixels and intensities
    • Color spaces
    • Histogram, histogram equalization, CLAHE
    Practical Workshop
    • Python, libs
    • Virtual environments
    • OpenCV basics
    • Data types
    • Image/video input
    • Channel separation
    • Color spaces
    Lecture 2
    Theory
    • Image filtering (blur, bilateral)
    • Background subtraction
    • Image gradients
    • Thresholding (global, adaptive, Otsu)
    • Morphological operations
    • Math: temporal and frequency domain filtering
    Practical Workshop
    • Thresholding
    • Filtering
    • Morphology
  • Week 2. Feature extraction: Basic
  • Week 3. Feature extraction: Advanced
  • Week 4. Description and matching
  • Week 5. Stereovision, multi-view geometry + computational photography
Part 2. Machine learning and deep learning for computer vision tasks
  • Week 6. Introduction to machine learning
  • Week 7. Introduction to neural networks
  • Week 8. Deep learning essentials
  • Week 9. Deep learning for practical CV problems
  • Week 10. Deep learning for image representation and generation
  • By the end of this course, you will be fully equipped to:

  • Choose and apply methods for image processing
  • Be able to implement and analyze basic methods for computer vision
  • Build efficient DL pipelines
  • Be able to pick the right DL architecture for specific CV problems
  • Be able to choose the right methods for specific CV tasks and analyze their possibilities and limitations

Meet Your Lecturers

  • Pavlo Vyplavin
    CTO at It-Jim

    Pavlo’s scientific experience for over 15 years in both industry and academia and a Ph.D. degree in radio physics helps him to drive technological development at It-Jim. Pavlo is an active contributor to the company’s educational program. He constantly delivers lectures and presentations on computer vision, machine learning, and artificial intelligence at international conferences. Within this course, Pavlo will be teaching the classical computer vision part.

  • Yurii Chyrka
    Head of ML at It-Jim

    Yurii is a Ph.D. in signal processing, has 10+ years of scientific experience and dozens of completed CV/ML projects of different scales. Apart from controlling and supporting a full cycle of AI project development, Yurii is a key speaker in multiple educational projects in the machine learning and deep learning fields. Yurii will be delivering the second part of the course dedicated to machine learning and deep learning for computer vision tasks.

Learning Process

  • Webinars
    Online webinars will be held two times a week. The lectures will be delivered either in Ukrainian or Russian. Every webinar will consist of a theoretical introduction, practical workshop and review of homework solutions.
  • Access to all materials
    The recordings of live webinars will be available in your account in LMS (Learning Management System) shortly after each lecture along with other materials including presentations, demos, hands-on exercises, useful links and resources.
  • Home tasks
    Each week, you will receive a home task to work through the material and get feedback from lecturers on your solutions. Additionally, you will get assignments in LMS to test your knowledge in a course content of a given week.

This course is for you if...

  • You are an experienced software developer who wants to switch to CV/ML/DL domain, or
  • You are a 4- or 5-year student of technical specialties with a passion for computer vision, or
  • You are a data scientist with little background in computer vision aiming to change that, or
  • You are looking for an extensive and solid base in computer vision.
To succeed in this course, you would need to:
  • Know Python,
  • Be familiar with the basics of linear algebra, calculus and probability.

Our Experience

In the last couple of years, we have been very actively involved in many educational projects including:

Let our students do the talking

“It was two hot months. The first month was devoted to classical approaches in image analysis, and the second one - to computer vision with neural networks. At the beginning of each lesson, we went over the best code implementations in homework assignments, and it was very helpful.

By the end of the course, I had gained structured knowledge about approaches in computer vision and working code implementations for my pet projects. Many thanks to the team for sharing their knowledge so generously and with dedication.”

Olena Ivina
Data Analyst

“Probably one of the best computer vision courses I've seen.

Nowadays CV is often identified with ML, so I liked the part of the course that touches on approaches without using machine learning the most. The material from the block on classical methods is hard to find in the public domain in such a clear and structured way.”

Viktoria Skorik
Junior Machine Learning Engineer at SoftServe

“I took part in this Computer Vision course in summer 2019.

Despite the fact that I was a newbie in this area, well-prepared lessons allowed me to progress quickly and it was really fun. After a few months of study on the course, I continued to learn more about CV and ML as part of the It-Jim team.

CV is an interesting sphere and this course can prove it.”

Ruslan Timchenko
Computer Vision Engineer

“During the internship, we listened to lectures on classical computer vision and deep learning in CV, took part in workshops and worked on our home tasks. The material was really interesting and useful. The workshops gave us lots of hints and facilitated a more in-depth search on the topics.

In the end, I had no doubts that I would follow the path of a computer vision engineer.”

Mykhailo Bichurin
Computer Vision Engineer

“The It-Jim CV internship course gives a deep understanding of what an image is, what we can do with it, and why. Much emphasis is placed on classical computer vision, the approaches of which are basic to CV.

Special kudos to the entire team!”

Oleksandr Khrystoforov
Data Scientist

“During the internship, we got a good overview of both classical and deep learning approaches to solving computer vision problems. The learning from foundations provides more options for fine-tuning complex algorithms. Each theoretical lesson was complemented by interesting homework with real-world tasks. It allowed me to dive deeper into the subject.

Moreover, we had after-homework discussions with a review of our solutions and valuable insights.”

Alina Albasova
AI/Machine Learning Engineer
FAQ

Students ask, we answer

  • How much time do I need to devote to the course to successfully complete it?
    Apart from 4-5 hours a week that you are going to spend for online classes, you will need another 15-20 hours to work on the homework, test assignments and explore additional materials that we will provide.
  • How do I interact with the lecturers?
  • If I don't like the course, will I get a refund?
  • If I miss a class, how do I catch up on the material?
  • Will I get a certificate of completion?
  • Will I have access to the course materials when I complete the course?

Reserve your spot now

Agreement
  • When
    October 5, 2021
  • How many
    15 spots left
  • Daryna