Advanced Representation Learning is a course designed to delve deeper into the fundamental concepts of representation learning and its applications. In this class, students will explore various representation learning techniques, including both classical and deep learning methods, and learn how to apply these techniques to solve complex problems in computer vision, natural language processing, audio, and other areas. By working on the research project component of the course, the students will develop novel methods and theories about representation learning and prepare manuscripts describing their findings. By the end of this course, the students will have a solid understanding of the state-of-the-art in representation learning and be able to apply these techniques to solve real-world problems.

• Time and location:
  • Tue & Thu, 12:45pm-2pm, Boyd 222
  • Wed, 12:40pm-1:30pm, Boyd 222
• Office hours:
  • Thu, 4pm-5pm, Boyd 804
• References:
  • Deep Learning by Ian Goodfellow, Yoshua Bengio, and Aaron Courville. Free
  • Dive into Deep Learning by Aston Zhang, Zachary C. Lipton, Mu Li, and Alexander J. Smola. Free
  • Computer Vision: Algorithms and Applications by Richard Szeliski. Free
  • “Machine Learning: a Probabilistic Perspective” by Kevin Murphy.
  • Foundations of Data Science by Avrim Blum, John Hopcroft, and Ravindran Kannan. Free
• Syllabus: PDF
• Learning outcomes
  1. Demonstrate understanding of machine learning and deep neural network fundamentals.
  2. Gain experience deploying deep learning models in computer vision, natural language processing, and audio domains.
• Contact: Annoucements will be made on eLC. You can also send an email to me at jinsun@uga.edu.