Course Descriptions and Schedule

Please note that course schedules may be amended due to low enrollment, faculty availability, and/or other factors.

MSDS 464-DL : Intelligent Systems and Robotics


Description

This course introduces reinforcement learning as an approach to intelligent systems, emphasizing applications such as robotic processes automation, conversational agents and robotics that mimic human behavior. Students implement intelligent agents to solve both discrete- and continuous-valued sequential decision-making problems. Students develop, debug, train, visualize, and customize programs in a variety of learning environments. The course reviews Markov decision processes, dynamic programming, temporal difference learning, Monte Carlo reinforcement learning, eligibility traces, the role of function approximation, and the integration of learning and planning. This is a case study and project-based course with a substantial programming component.

Recommended prior course: MSDS 458-DL Artificial Intelligence and Deep Learning.

Prerequisites: (1) MSDS 420-DL Database Systems and Data Preparation or CIS 417 Database Systems Design and Implementation and (2) MSDS 422-DL Practical Machine Learning or CIS 435 Practical Data Science Using Machine Learning.


Summer 2021
Start/End DatesDay(s)TimeBuildingSection
06/21/21 - 08/29/21Optional Sync Th
7 – 9:30 p.m. 55
InstructorCourse LocationStatusCAESAR Course ID
Bharadwaj, Shreenidhi
Online
Open
^ Back to top ^