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Course

Introduction to Deep Learning for Computer Vision

CSE-41388

 

Deep Learning is a layered version of Machine Learning that began in the 1980s with Image Processing Engineering and the popular Computer Vision (CV) application.  Teaching computer prgrams to see and understand the content of images and videos is CV as the base model architecture of the course. However, Deep Learning has broadened its applications in the past decade and is now an extension of other Data Science frameworks.

This course is a comprehensive introduction to Deep Learning as an extension of Supervised Learning, Unsupervised Learning, Reinforcement Learning, Graph Data Science, Natural Language Processing, and Time Series Analysis.  The first two weeks are preparation for the implementations with a clear set of definitions, characteristics, and methodologies of today’s Deep Learning. Then, demonstrations begin with Multilayer Perceptron and Feedforward Artificial Neural Networks (ANN), and we learn the fundamental techniques of Hyperparameter Tuning using Tensorflow and Keras.  The Convolutional Neural Network (CNN) model architecture is the key and most widely deployed Deep Neural Network architecture for computer vision, as the first half of the course focuses on learning the building blocks and different model architectures.

The second half expands to the introduction of Deep Neural Network architectures such as Deep Neural Networks (DNN), Recurrent Neural Networks (RNN), Generative Adversarial Networks (GAN), and all the extensions of other Data Science frameworks.  The breadth of applications beyond CV is augmented reality, autonomous driving, unmanned Aerial Vehicles (UAV), forecasting human behavior, real estate image classification, stock market prediction, and many more.


Course Highlights: 

  • Deep Learning fundamentals and its application in real world
  • Application of deep learning in computer vision
  • Implementation of training of neural networks
  • Compression and pruning techniques

Course Learning Outcomes: 

  • Learn fundamentals of deep learning and the widespread network architectures employed in the real world for computer vision applications
  • Implement and train your Neural Networks to understand how Deep Learning is used in Computer Vision and other applications
  • Apply course content to develop, train, and fine-tune the Neural Network for your selected computer vision application and other applications
  • Learn new research and development area of Deep Learning running at the edge by understanding the techniques of compression and pruning and tool for deploying the deep learning model on the platform, such as smartphones

Prerequisites: Basic proficiency in programming, college calculus, linear algebra, and probability and statistics

Course typically offered: Online during our Winter and Summer academic quarters

Next steps: You can continue learning by following Machine Learning Methods or Technical Aspects of Artificial Intelligence certificate program

Contact: For more information about this course, please email unex-techdata@ucsd.edu
 

Course Information

Online
3.00 units
$775.00

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This course applies to these programs

Technical Aspects of Artificial Intelligence Machine Learning Methods

Course sessions

Closed

Section ID:

185520

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: 9/30/2024

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.
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Section ID:

185667

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:

No textbook required.

Policies:

  • No refunds after: 1/20/2025

Schedule:

No information available at this time.
Add To Cart

Instructor: Chung-Chi Tsai

Chung-Chi Tsai
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