Course Summary
This course provides an in-depth exploration of sample-based learning methods, including active learning, transfer learning, and online learning. Through real-world case studies, students will learn how to apply these methods to a variety of domains, from healthcare to finance.Key Learning Points
- Gain a deep understanding of sample-based learning methods
- Apply these methods to real-world problems in various domains
- Learn from real-world case studies
Related Topics for further study
Learning Outcomes
- Understand the theory and practical application of sample-based learning methods
- Apply sample-based learning methods to real-world problems
- Evaluate the effectiveness of sample-based learning methods
Prerequisites or good to have knowledge before taking this course
- Basic understanding of machine learning concepts
- Familiarity with programming languages such as Python
Course Difficulty Level
IntermediateCourse Format
- Online
- Self-paced
Similar Courses
- Reinforcement Learning
- Deep Learning
- Applied Data Science with Python
Related Education Paths
Notable People in This Field
- Andrew Ng
- Yann LeCun
Related Books
Description
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.
Outline
- Welcome to the Course!
- Course Introduction
- Meet your instructors!
- Reinforcement Learning Textbook
- Read Me: Pre-requisites and Learning Objectives
- Monte Carlo Methods for Prediction & Control
- What is Monte Carlo?
- Using Monte Carlo for Prediction
- Using Monte Carlo for Action Values
- Using Monte Carlo methods for generalized policy iteration
- Solving the Blackjack Example
- Epsilon-soft policies
- Why does off-policy learning matter?
- Importance Sampling
- Off-Policy Monte Carlo Prediction
- Emma Brunskill: Batch Reinforcement Learning
- Week 1 Summary
- Module 1 Learning Objectives
- Weekly Reading
- Chapter Summary
- Graded Quiz
- Temporal Difference Learning Methods for Prediction
- What is Temporal Difference (TD) learning?
- Rich Sutton: The Importance of TD Learning
- The advantages of temporal difference learning
- Comparing TD and Monte Carlo
- Andy Barto and Rich Sutton: More on the History of RL
- Week 2 Summary
- Module 2 Learning Objectives
- Weekly Reading
- Practice Quiz
- Temporal Difference Learning Methods for Control
- Sarsa: GPI with TD
- Sarsa in the Windy Grid World
- What is Q-learning?
- Q-learning in the Windy Grid World
- How is Q-learning off-policy?
- Expected Sarsa
- Expected Sarsa in the Cliff World
- Generality of Expected Sarsa
- Week 3 Summary
- Module 3 Learning Objectives
- Weekly Reading
- Chapter summary
- Practice Quiz
- Planning, Learning & Acting
- What is a Model?
- Comparing Sample and Distribution Models
- Random Tabular Q-planning
- The Dyna Architecture
- The Dyna Algorithm
- Dyna & Q-learning in a Simple Maze
- What if the model is inaccurate?
- In-depth with changing environments
- Drew Bagnell: self-driving, robotics, and Model Based RL
- Week 4 Summary
- Congratulations!
- Module 4 Learning Objectives
- Weekly Reading
- Chapter Summary
- Text Book Part 1 Summary
- Practice Assessment
Summary of User Reviews
Learn about sample-based learning methods with this online course from Coursera. Users have praised the course for its practical approach and real-world examples. The course has received positive reviews overall.Key Aspect Users Liked About This Course
Practical approach and real-world examplesPros from User Reviews
- Clear and concise explanations of complex concepts
- Interactive assignments and quizzes to reinforce learning
- Great for beginners with no prior knowledge of the subject
- Flexible schedule allows for self-paced learning
- Good value for the price
Cons from User Reviews
- Lack of depth in some topics
- Limited interaction with instructors
- Some technical issues with the platform
- Not suitable for advanced learners
- No certificate of completion without paying for the verified track
Martha White
4.7 Raiting
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