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Sample-based Learning Methods

In this course, you will learn about several algorithms that can learn near optimal policies based on trial and error interaction with the environment---learning from the agent’s own experience. Learning from actual experience is striking because it requires no prior knowledge of the environment’s dynamics, yet can still attain optimal behavior. We will cover intuitively simple but powerful Monte Carlo methods, and temporal difference learning methods including Q-learning. We will wrap up this course investigating how we can get the best of both worlds: algorithms that can combine model-based planning (similar to dynamic programming) and temporal difference updates to radically accelerate learning.

Created by Alberta Machine Intelligence Institute


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What you’ll learn


In this educational resource you can gain confidence with the compentencies sought from companies these days. The most relevant technique within the educational resource that is frequently mentioned from organizations is Data Modeling. You will also learn about Programming Skills, a trait commonly included in job advertisements.

Who will benefit?


Contrasting the description from this learning opportunity with nearly 10,000 data-related job descriptions, we discover that those in or pursuing Data Scientist roles would benefit the most.