Course Overview

The course is all about learning the several algorithms that are based on trial and error interactions with the environment. Also, the course will cover intuitively simple and more powerful Monte Carlo methods including Q learning. The course will end with investigating algorithms that combine with model-based planning and temporal difference readily accelerating the learning.

In the end, the students will be able to understand temporal differences and Monte Carlo strategies, and they will understand the importance of exploration, and the connection between Monte Carlo and dynamic programming. Furthermore, they will be able to implement the TD algorithm, estimate value functions, and apply expected sarsa and Q learning as well. Finally, learners will be able to handle a made-based approach to RL, called Dyna that uses simulated experience and conducts an empirical study to see the improvement in the sample efficiency while using Dyna.

• Function approximation
• Artificial intelligence
• Reinforcement learning
• Machine learning

Requirements

• Basic knowledge of machine learning
• A computer or laptop with internet connectivity.
• Learn about techniques of sample based learning in approximately 22 hours.
Flexible work schedule.

Accessto Career Resources with Sample-based Learning Methods Course

Enroll Free

Enhanced Employability:

An outstanding Sample-based Learning Methods Program that increases your chances of landing the best jobs.

Competitive Advantage:

An opportunity to stand out from the competition and make a more significant impression on potential employers.

Skill Validation:

Robust validation of sample-based learning skills confirms that you can complete all related tasks.

Networking Opportunities:

Managing Sample-based Learning Methods courses provides ample networking opportunities for participants. Learners can connect with classmates and industry professionals through cooperative exercises, group discussions, and interactive seminars.