Course Description

One first step towards developing Artificial intelligence (AI)-powered machines is to efficiently and effectively process sensory data and abstract them with symbolic or numerical descriptions, thereby generalizing their concepts and benefiting higher-level perception and decision-making tasks. Deep learning, a subset of machine learning, has emerged as a powerful approach to achieving this goal, particularly in the domain of computer vision, and it has led to human-level performance in various visual recognition tasks.

This course is designed to provide a broad introduction of deep learning techniques and their application to computer vision problems. Students will explore cutting-edge deep neural networks and learn how to leverage them to analyze, interpret, and understand visual data. The topics includes:

  • Linear classifiers
  • Stochastic gradient descent
  • Fully-connected networks
  • Convolutional networks
  • Recurrent networks
  • Attention and transformers
  • Object detection
  • Image segmentation
  • Generative models
  • And others


Quick Information

Class time: 11:00 am - 1:30 pm. Friday
Location: Shelby Center 1122
Syllabus: The syllabus has detailed course policies
Schedule: TBD
Office Hours: 1:30 pm - 2:30 pm. Friday
Assignments: [A1] [A2] [A3] [A4]
Project: [details]