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Lead Instructor(s)
Date(s)
Jul 06 - 10, 2020
Registration Deadline
Location
On Campus
Course Length
5 Days
Course Fee
$5,500
CEUs
3.1
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This course may be taken individually or as part of the Professional Certificate Program in Machine Learning & Artificial Intelligence.

Deep learning innovations are driving exciting breakthroughs in the field of computer vision. Robots and drones not only “see”, but respond and learn from their environment. Autonomous cars avoid collisions by extracting meaning from patterns in the visual signals surrounding the vehicle. 

This course covers the latest developments in vision AI, with a sharp focus on advanced deep learning methods, specifically convolutional neural networks, that enable smart vision systems to recognize, reason, interpret and react to images with improved precision. 

Participant Takeaways

Participants will explore the latest developments in neural network research and deep learning models that are enabling highly accurate and intelligent computer vision systems capable of understanding and learning from images. We will start from fundamental topics in image modeling, including image formation, feature extraction, and multiview geometry, then move on to the latest applications in object detection, 3D scene understanding, vision and language, image synthesis, and vision for embodied agents. By the end, participants will:

  • Be familiar fundamental concepts and applications in computer vision
  • Grasp the principles of state-of-the art deep neural networks 
  • Understand low-level image processing methods such as filtering and edge detection
  • Gain knowledge of high-level vision tasks such as object recognition, scene recognition, face detection and human motion categorization 
  • Develop practical skills necessary to build highly-accurate, advanced computer vision applications

Who Should Attend

Designed for data scientists, engineers, managers and other professionals looking to solve computer vision problems with deep learning, this course is applicable to a variety of fields, including: 

  • Automotive
  • Healthcare & Pharmaceutical
  • Government & Military
  • Robotics
  • Retail
  • Media & Advertising
  • Security
  • Science

Requirements

Laptops with which you have administrative privileges along with Python installed are required for this course.

Participants should have experience in programming with Python, as well as experience with linear algebra, calculus, statistics, and probability.

Program Outline

This course meets 9:00 am - 5:00 pm each day.

Day One:

  • 9:00am: Introduction to computer vision (Torralba)
  • 10:00am: Cameras and image formation (Torralba)
  • 11:00am: Coffee break
  • 11:15am: Introduction to machine learning (Isola)
  • 12:15pm: Lunch (provided)
  • 1:30pm: The problem of generalization (Isola)
  • 2:30pm: Neural networks (Isola)
  • 3:30pm: Coffee break
  • 3:45pm: Tutorial on Pytorch part 1
  • 5:00pm: Adjourn

Day Two:

  • 9:00am: Filters, pyramids, and CNNs (Torralba)
  • 10:00am: Stochastic gradient descent (Torralba)
  • 11:00am: Coffee break
  • 11:15am: SGD exercise
  • 12:15pm: Lunch break (on your own)
  • 1:30pm: Representation learning (Isola)
  • 2:30pm: Temporal processing and RNNs (Isola)
  • 3:30pm: Coffee break
  • 3:45pm: Tutorial on Pytorch part 2
  • 5:00pm: Adjourn

Day Three:

  • 9:00am: Multiview geometry (Torralba)
  • 10:00am: 3D deep learning (Torralba)
  • 11:00am: Coffee break
  • 11:15am: Scene understanding (Torralba)
  • 12:15pm: Lunch break (on your own)
  • 1:30pm: Vision and language (Isola)
  • 2:30pm: Image synthesis and generative models part 1 (Isola)
  • 3:30pm: Coffee break
  • 3:45pm: Lab exercise
  • 5:00pm: Adjourn

Day Four:

  • 9:00am: Image synthesis and generative models part 2 (Isola)
  • 10:00am: Vision for embodied agents part 1 (Isola)
  • 11:00am: Coffee break
  • 11:15am: Vision for embodied agents part 2 (Isola)
  • 12:15pm: Lunch break (on your own)
  • 1:30pm: Datasets, bias, and adaptation (Torralba)
  • 2:30pm: Robustness and security (Torralba)
  • 3:30pm: Coffee break
  • 3:45pm: Practical tips and closing remarks
  • 5:00pm: Adjourn

Links & Resources

Content

The type of content you will learn in this course, whether it's a foundational understanding of the subject, the hottest trends and developments in the field, or suggested practical applications for industry.

Fundamentals: Core concepts, understandings, and tools - 40%|Latest Developments: Recent advances and future trends - 40%|Industry Applications: Linking theory and real-world - 20%
40|40|20
Delivery Methods

How the course is taught, from traditional classroom lectures and riveting discussions to group projects to engaging and interactive simulations and exercises with your peers.

Lecture: Delivery of material in a lecture format - 50%|Discussion or Groupwork: Participatory learning - 30%|Labs: Demonstrations, experiments, simulations - 20%
50|30|20
Levels

What level of expertise and familiarity the material in this course assumes you have. The greater the amount of introductory material taught in the course, the less you will need to be familiar with when you attend.

Introductory: Appropriate for a general audience - 30%|Specialized: Assumes experience in practice area or field - 50%|Advanced: In-depth explorations at the graduate level - 20%
30|50|20