What will you learn in this Sample-based Learning Methods Course
Understand Temporal-Difference (TD) learning and Monte Carlo methods as strategies for estimating value functions from sampled experience.
Implement and apply TD algorithms, including Expected Sarsa and Q-learning, for control tasks.
Differentiate between on-policy and off-policy control methods.
Explore planning with simulated experience and implement the Dyna algorithm to enhance learning efficiency
Program Overview
1. Monte Carlo Methods
⏳ 4 hours
Learn about Monte Carlo methods for prediction and control, using sampled returns to estimate value functions without requiring knowledge of the environment’s dynamics.
2. Temporal-Difference Learning
⏳ 4 hours
Explore TD learning, which combines aspects of Monte Carlo and Dynamic Programming methods, allowing for learning from incomplete episodes.
3. TD Control Methods
⏳ 4 hours
Delve into control methods like Sarsa, Expected Sarsa, and Q-learning, understanding their applications and differences
4. Planning and Learning with Tabular Methods
⏳ 4 hours
Investigate how to integrate planning and learning using the Dyna architecture, which combines model-based and model-free approaches.
5. Final Project
⏳ 6 hours
Apply the concepts learned to implement and analyze reinforcement learning algorithms in practical scenarios.
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Job Outlook
Prepares learners for roles such as Machine Learning Engineer, AI Researcher, and Data Scientist.
Applicable in industries like robotics, gaming, finance, and autonomous systems.
Enhances employability by providing practical skills in reinforcement learning and decision-making algorithms.
Supports career advancement in fields requiring expertise in adaptive systems and intelligent agents.
Specification: Sample-based Learning Methods
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FAQs
- No advanced math is required, but basic statistics is useful.
- The course explains ideas in an accessible, application-driven way.
- Students will learn to focus on how sampling impacts model performance.
- Sample-based learning focuses on drawing subsets of data for training.
- It’s useful for improving efficiency with large datasets.
- It enhances model generalization by reducing overfitting.
- Fraud detection in banking using sampled transaction data.
- Clinical trial design with representative subsets of patient groups.
- Market research with survey sampling for faster decision-making.
- Yes, sampling is used in deep learning for mini-batch training.
- Helps handle massive datasets efficiently.
- Improves performance while keeping training manageable.
- Builds practical skills for handling large, messy datasets.
- Prepares learners for roles where efficiency and scalability matter.
- Adds a specialized technique to strengthen a data science portfolio.
