Deep-Learning and Reinforcement Learning By All lab members

Spring 2022

    The purpose of the seminar is to learn deep and reinforcement learning techniques mainly by working with ROS and creating a high quality of open sources for the robotics community. Our students can learn to analyze problems, design machine learning (ML) models, and apply them to the robot-related problem space. The seminar covers learning techniques and ROS spaces: students can utilize ML libraries with knowledge and ROS platforms.

Following is the 11 week Lab Schedule:
Week 1: Deep Learning Basics by Shyam Sundar Kannan
Week 2: How to Program an L-layer Neural Network in Python by Vishnunandan Venkatesh
Week 3: Hyperparameter Tuning by Wonse Jo
Week 4: Convolutional Neural Networks by Go-Eum Cha
Week 5: Deep Learning Course Project and Seminar Review by All members
Week 6: Reinforcement Learning Problem by Su Sun
Week 7: Dynamic Programming Problem by Roman Ibrahimov
Week 8: Monte Carlo Methods by Jeremy Pan
Week 9: Temporal-Difference Methods by Ruiqi Wang
Week 10: Reinforcement Learning: Course Project by Pou Hei Chan
Week 11: Seminar Review