Course Description
Generative models are widely used in many subfields of AI and Machine Learning. Recent advances in parameterizing these models using deep neural networks, combined with progress in stochastic optimization methods, have enabled scalable modeling of complex, high-dimensional data including images, text, and speech. In this course, we will study the probabilistic foundations and learning algorithms for deep generative models, including variational autoencoders, generative adversarial networks, autoregressive models, normalizing flow models, and energy-based models. The course will also discuss application areas that have benefitted from deep generative models, including computer vision, speech and natural language processing, and reinforcement learning.
Note 1
Zoom link for the first month of classes is
here.
Note 2
PTEs have been assigned to the students. You are welcome to sit through or watch videos even if you are not formally enrolled. Please send me your email id ending with @ucla.edu to be added to bruinlearn/canvas.
Health and Safety Expectation (COVID-19 protocols)
Following UCLA’s policies, everyone is required to wear a mask indoors, regardless of vaccination status. This includes any in-person lectures or office hour sessions. Some community members may have preferences that go beyond the requirements; it is important that we treat each others' preferences with respect and care. You can find the most current policies on campus protocols on the
UCLA COVID-19 site.
Lecture Attendance
While we do not require in-person lecture attendance, students are encouraged to join the live lecture. To accommodate various circumstances, lecture recordings will also be available on Canvas shortly following the lecture.
UCLA Honor Code
Students are free to form study groups and may discuss homework in groups. However, each student must write down the solutions and code from scratch independently, and without referring to any written notes from the joint session. In other words, each student must understand the solution well enough in order to reconstruct it by him/herself. It is an honor code violation to copy, refer to, or look at written or code solutions from a previous year, including but not limited to: official solutions from a previous year, solutions posted online, and solutions you or someone else may have written up in a previous year. Furthermore, it is an honor code violation to post your assignment solutions online, such as on a public git repo.
The UCLA Honor Code can be found
here
