The CS 238 Stanford course, also known as "Decision Making under Uncertainty," is a graduate-level class offered by the Stanford University Department of Computer Science. This course focuses on the principles and techniques of decision making under uncertainty, which is a crucial aspect of artificial intelligence and machine learning. The primary goal of this course is to provide students with a comprehensive understanding of the theoretical foundations and practical applications of decision making under uncertainty.
Course Overview
CS 238 Stanford covers a wide range of topics, including probabilistic graphical models, decision theory, and reinforcement learning. The course begins with an introduction to the basics of probability theory and then delves into the concepts of Bayesian networks and influence diagrams. Students learn how to represent and reason about uncertain relationships between variables using these probabilistic graphical models. The course also explores decision-theoretic concepts, such as expected utility theory and decision-making under risk and uncertainty.
Probabilistic Graphical Models
Probabilistic graphical models are a key component of the CS 238 Stanford course. These models provide a powerful framework for representing and reasoning about complex probabilistic relationships between variables. Students learn how to construct and manipulate Bayesian networks and Markov networks, and how to perform inference and learning tasks using these models. The course also covers conditional random fields and other advanced topics in probabilistic graphical models.
| Topic | Description |
|---|---|
| Bayesian Networks | A probabilistic graphical model for representing uncertain relationships between variables |
| Influence Diagrams | A decision-theoretic graphical model for representing decision-making problems under uncertainty |
| Decision Theory | A framework for making decisions under risk and uncertainty |
Decision Theory and Reinforcement Learning
The second half of the CS 238 Stanford course focuses on decision theory and reinforcement learning. Students learn about the principles of rational decision making and how to apply these principles to real-world problems. The course covers Markov decision processes and reinforcement learning algorithms, including Q-learning and SARSA. Students also learn about deep reinforcement learning and how to apply these techniques to complex decision-making problems.
Reinforcement Learning Algorithms
Reinforcement learning algorithms are a key component of the CS 238 Stanford course. These algorithms provide a powerful framework for learning optimal decision-making policies in complex environments. Students learn how to implement and apply Q-learning and SARSA algorithms, as well as more advanced techniques such as deep Q-networks and policy gradient methods.
- Q-learning: a model-free reinforcement learning algorithm for learning optimal decision-making policies
- SARSA: a model-free reinforcement learning algorithm for learning optimal decision-making policies
- Deep Q-networks: a type of deep reinforcement learning algorithm for learning optimal decision-making policies
What is the main focus of the CS 238 Stanford course?
+The main focus of the CS 238 Stanford course is decision making under uncertainty, with a emphasis on probabilistic graphical models, decision theory, and reinforcement learning.
What are some of the key topics covered in the course?
+Some of the key topics covered in the course include Bayesian networks, influence diagrams, decision theory, Markov decision processes, and reinforcement learning algorithms such as Q-learning and SARSA.
In conclusion, the CS 238 Stanford course provides a comprehensive introduction to decision making under uncertainty, with a focus on probabilistic graphical models, decision theory, and reinforcement learning. The course covers a wide range of topics and provides students with a deep understanding of the theoretical foundations and practical applications of decision making under uncertainty.
