|Instructor:||Andrew L. Mackey|
|Meeting Times:||Tuesdays/Thursdays at 9:30 - 10:45 pm|
|Lab Assistant:||Kyle Kelly|
|Lab Times:||Fridays at 2:00 pm - 4:00 pm|
This course focuses on advancements to recent advancements to neural network architectures. It examines deep neural networks and their applications to the fields of artificial intelligence, natural language processing, machine learning, and computer graphics and vision.
Students are required to have a prior background in artificial intelligence or machine learning, probability theory, matrix algebra, and calculus.
The follow list contains courses that I have taught within the past few years.
|1||Course Introduction||Complete the
|2||Deep Learning Fundamentals||Complete the
|3||Deep Learning Fundamentals (cont.)|
|4||Convolutional Neural Networks||Complete the
|5||Convolutional Neural Networks (cont.)|
|6||Recurrent Neural Networks||Complete the
|7||Recurrent Neural Networks (cont.)|
|8||Attention and Transformers|
|10||Generative Models (cont.)|
|11||Reinforcement Learning||Complete the
|12||Reinforcement Learning (cont.)|
|13||Advanced Topics in Deep Learning|
|14||Advanced Topics in Deep Learning (cont.)|
|15||Final Project Presentations|
|16||Review and Final Examination|
No reading material has been assigned. However, the following textbooks are optional and may supplement the material in this class:
All students will be required to complete a final project. Students will go above and beyond existing current knowledge to attempt a novel research project. Students will investigate new architectures to solve an existing problem, adapt some methodology from a different problem to solve an existing problem, or define and solve a new problem of interest.
The following resources are available for students to use.
Review the course website for more information