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Self-Supervised Learning

Registration Now Open!

Transform Computer Vision with Self-Supervised Learning

Course Description

Dive into the cutting-edge world of self-supervised learning (SSL) for computer vision in this dynamic and hands-on course. SSL is revolutionizing AI by enabling models to learn from vast amounts of unlabeled data, making it a true game-changer.

In this course, you’ll explore popular SSL methods like SimCLR, MoCo, BYOL, and Vision Transformers (DINO), while gaining hands-on experience using PyTorch to build and train your own models. Engage in interactive coding sessions and apply SSL techniques to real-world datasets through project-based assessments, ensuring you gain both theoretical knowledge and practical expertise. Whether you’re an AI enthusiast or a professional looking to advance your skills, this course will equip you with the tools to create more efficient and scalable computer vision models. Join us and be at the forefront of AI innovation!

This course is one of 6 courses in the Advanced AI Techniques pilot micro-credential pathway offered by the Translational AI Center at Iowa State University.

Advanced AI Techniques Pathway Courses:

Prerequisite

  • Python programming
  • Fundamental idea of Computer Vision

Intended Audience

This course is aimed at software engineers, data scientists, data engineers, data analysts, research scientists, and developers who wish to advance their understanding of computer vision. Previous participants have included professionals from leading technology and AgriTech companies.

  • Learning Outcomes
  • Assessments
  • Course Outline

By the end of the course, you should be able to:

  • Understand the fundamentals of self-supervised learning (SSL) and its role in computer vision, including classical and recent methods. 
  • Implement SSL methods, including contrastive, clustering-based, and generative approaches, using PyTorch.
  • Apply and evaluate SSL models for various downstream tasks such as image classification, object detection, and segmentation.
  • Optimize and deploy SSL models using techniques like quantization, pruning, and TorchScript/ONNX for real-world applications.
  • Explore advanced SSL techniques, future trends, and their applications in different domains of computer vision.
  • 3 Quizzes: Test comprehension of fundamental self-supervised learning concepts. 
  • 1 Coding Assignment: Implementing both classical and state-of-the-art SSL methods. Also, fine-tuning SSL models for different downstream tasks and evaluating their performance.
  • Module 1: Fundamentals of Self-Supervised Learning
  • Module 2: Implementation of SSL Methods
  • Module 3: Applying SSL Models to Downstream Tasks
  • Module 4: Optimizing and Deploying SSL Models
  • Module 5: Advanced SSL Techniques and Future Trends

Course Procedures

The course starts on November 4, 2024. All coursework must be completed before the course ends on December 15, 2024. The approximate time to complete this course is 16 hours. You can complete the modules at your own pace.

Live Zoom meetings will be conducted for interactive coding sessions. A suitable time for these live sessions will be determined through a group poll. The recordings of those sessions will be available soon after each meeting.

You will receive a micro-credential badge upon completing the assessments at the end.

Course Materials

Course materials are provided within the course. No additional purchase is required.

Contact Information

Contact isopd@iastate.edu for more information.

Course Developer

Translational_AI Center

The Translational AI Center breaks down disciplinary silos to bring together core Iowa State artificial intelligence researchers and subject matter experts interested in applying new technologies to their work. For more information, visit Translational AI Center at Iowa State University

 

Registration Cost: $750.00 $500 USD
(Initial Promo)
*$300 Student & Government Employee

Course Hours: 16 hours

Course Start Date: November 4, 2024

Last Day to Register: November 8, 2024

Course End Date: December 15, 2024

Course Access Time: 41 Days

*At the time of registration, you’ll be asked to create an account for this course. Use an email address ending in “.edu” or “.gov” to receive a discount. $200.00 will be immediately credit back after purchase.

Instructor

 

Zaki Jubery Profile PictureZaki Jubery, Research Scientist

Zaki Jubery is a research scientist in the Translational AI Center (TrAC) at Iowa State University. His research interests are in (i) High-throughput phenotyping (ii) Crop modeling (iii) Image processing (iv) Applied machine learning in agriculture.

Zaki works on integrating engineering tools into various agricultural applications. He has been dedicated to pioneering research in this field since September 2013.

Zaki earned his Ph.D. in Mechanical Engineering from Washington State University and completed a postdoctoral fellowship at the University of Illinois Urbana-Champaign. Before transitioning to agriculture, his background includes designing, simulating, and manufacturing point-of-care microfluidics sensors for biomedical and industrial applications.