Coronavirus (COVID-19) Update
Fall classes are offered remotely—either live-online with an instructor, entirely self-paced, or in a blended online format. Please check our coronavirus update page for our latest announcements.
Reinforcement Learning and Advanced Topics in AI
The purpose of this course is to illustrate how to combine three important aspects of AI: deep learning (DL), natural language processing (NLP), and reinforcement learning (RL).
The first portion of this course contains recent advances in DL in image classification, such as Mask R-CNNs. The second portion introduces NLP and then combines DL and NLP. The third portion of this course introduces RL and combines RL with DL, which is called deep reinforcement learning (DRL).
These combined technologies have driven improvements in NLP, RL, and a plethora of other emerging fields. After completing this course you will be in a position to decide which of these specializations you want to pursue.
At the conclusion of the course, the student should be able to:
- Explain the advantages of the MASK R-CNN algorithm.
- Summarize how a bi-long short-term memory (LSTM) differs from a standard LSTM.
- Explain how n-grams work.
- Describe the bidirectional encoder representations from transformers (BERT) architecture.
- Describe Q Learning, models, and policies.
- Explain the purpose of the Bellman equation.
- Discuss the advantages and disadvantages of reinforcement learning.
- Explain how the epsilon-greedy algorithm differs from a pure greedy algorithm.
- Discuss how deep learning enhances reinforcement learning.
Knowledge of Python, Keras and some TensorFlow knowledge is required for code samples that are discussed in class and for coding-related assignments. Students need to be comfortable with an editor of their choice and the operating system of their laptop. MacBook is preferred.
Sections Open for Enrollment:
|Date:||Start Time:||End Time:||Meeting Type:||Location:|
|Thu, 10-08-2020||6:30 p.m.||9:30 p.m.||Live-Online||ONLINE|
|Thu, 10-15-2020||6:30 p.m.||9:30 p.m.||Live-Online||ONLINE|
|Thu, 10-22-2020||6:30 p.m.||9:30 p.m.||Live-Online||ONLINE|
|Thu, 10-29-2020||6:30 p.m.||9:30 p.m.||Live-Online||ONLINE|
|Thu, 11-05-2020||6:30 p.m.||9:30 p.m.||Live-Online||ONLINE|
|Thu, 11-12-2020||6:30 p.m.||9:30 p.m.||Live-Online||ONLINE|
|Thu, 11-19-2020||6:30 p.m.||9:30 p.m.||Live-Online||ONLINE|
|Thu, 12-03-2020||6:30 p.m.||9:30 p.m.||Live-Online||ONLINE|
|Thu, 12-10-2020||6:30 p.m.||9:30 p.m.||Live-Online||ONLINE|
|Thu, 12-17-2020||6:30 p.m.||9:30 p.m.||Live-Online||ONLINE|
Ask us any questions you may have about this course.