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Course

Introduction to Deep Learning for Computer Vision

CSE-41388

Understanding How Deep Learning Powers Visual Intelligence

Deep learning has transformed computer vision by enabling machines to interpret and understand images and videos with high accuracy. Originally rooted in image processing and supervised learning, deep learning now extends across data science fields such as natural language processing, reinforcement learning, and time series analysis.

This course provides a comprehensive introduction to deep learning with a strong focus on computer vision applications. You will begin with core concepts and methodologies, followed by hands-on implementation of neural networks including multilayer perceptrons and convolutional neural networks (CNNs). Using TensorFlow and Keras, you will explore hyperparameter tuning, model architectures, and practical training techniques.

The course also introduces advanced deep learning architectures such as recurrent neural networks (RNNs) and generative adversarial networks (GANs), and examines real-world applications including augmented reality, autonomous systems, image classification, and predictive analytics. You will gain practical experience in building, training, and optimizing deep learning models for computer vision and beyond.


Course Highlights

  • Fundamentals of deep learning and neural network architectures
  • Applications of deep learning in computer vision
  • Implementation and training of neural networks using TensorFlow and Keras
  • Convolutional neural networks (CNN) for image recognition and classification
  • Hyperparameter tuning and model optimization
  • Introduction to DNN, RNN, and GAN architectures
  • Model compression and pruning techniques for edge deployment

Course Benefits

  • Build a strong foundation in deep learning for computer vision
  • Gain hands-on experience training and fine-tuning neural networks
  • Apply deep learning techniques to real-world image and video problems
  • Learn how to optimize models for deployment on mobile and edge devices
  • Prepare for advanced studies and careers in AI and computer vision
  • Credit earned may be applied toward an academic degree or professional credential, subject to the approval of the receiving institution(s)

Course Details and Next Steps

Who Should Take This Course?

  • Aspiring machine learning and computer vision engineers
  • Data scientists and AI practitioners
  • Software developers interested in image and video analysis
  • University students in computer science, engineering, or related fields
  • Researchers exploring deep learning applications
  • Professionals seeking practical skills in computer vision and neural networks

Course Information

Online
3.00 units
$850.00

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This course applies to these programs

Technical Aspects of Artificial Intelligence Machine Learning Methods

Course sessions

Closed

Section ID:

194467

Class type:

Online Asynchronous.

This course is entirely web-based and to be completed asynchronously between the published course start and end dates. Synchronous attendance is NOT required.
You will have access to your online course on the published start date OR 1 business day after your enrollment is confirmed if you enroll on or after the published start date.

Textbooks:

All course materials are included unless otherwise stated.

Policies:

  • No refunds after: 1/19/2026

Schedule:

No information available at this time.
Closed

Instructor: John Foxworthy

John Foxworthy
John Thomas Foxworthy is a Data Science Veteran with 20 years of professional experience with Consulting Companies, Big Banks, and Hedge Funds.  He completed his Master of Science in Data Science from Northwestern University with a Thesis on Deep Learning Forecasting using Artificial Intelligence for numerical data, images, and text.  His Bachelor's degree is from the University of California, Los Angeles, from the Department of Economics, with a Thesis on the Limits of Econometric Modeling.
Full Bio
Add To Cart

Section ID:

196334

Class type:

Online Asynchronous.

This course is entirely web-based and to be completed asynchronously between the published course start and end dates. Synchronous attendance is NOT required.
You will have access to your online course on the published start date OR 1 business day after your enrollment is confirmed if you enroll on or after the published start date.

Textbooks:

All course materials are included unless otherwise stated.

Policies:

  • No refunds after: 4/6/2026

Schedule:

No information available at this time.
Add To Cart

Instructor: John Foxworthy

John Foxworthy
John Thomas Foxworthy is a Data Science Veteran with 20 years of professional experience with Consulting Companies, Big Banks, and Hedge Funds.  He completed his Master of Science in Data Science from Northwestern University with a Thesis on Deep Learning Forecasting using Artificial Intelligence for numerical data, images, and text.  His Bachelor's degree is from the University of California, Los Angeles, from the Department of Economics, with a Thesis on the Limits of Econometric Modeling.
Full Bio