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Thad Starner
Enhance Your AI Skills with Adversarial Training. Learn multi-agent environment search using game theory's minimax theorem. Start learning today with Udacity!
Prerequisite details
To optimize your success in this program, we've created a list of prerequisites and recommendations to help you prepare for the curriculum. Prior to enrolling, you should have the following knowledge:
Read more
Enhance Your AI Skills with Adversarial Training. Learn multi-agent environment search using game theory's minimax theorem. Start learning today with Udacity!
Prerequisite details
To optimize your success in this program, we've created a list of prerequisites and recommendations to help you prepare for the curriculum. Prior to enrolling, you should have the following knowledge:
Read more
Enhance Your AI Skills with Adversarial Training. Learn multi-agent environment search using game theory's minimax theorem. Start learning today with Udacity!
Prerequisite details
To optimize your success in this program, we've created a list of prerequisites and recommendations to help you prepare for the curriculum. Prior to enrolling, you should have the following knowledge:
- Object-oriented Python
- Constraint satisfaction problems
- Linear algebra
- Search algorithms
- Basic descriptive statistics
- Basic calculus
- Command line interface basics
- Basic probability
- Optimization algorithms
You will also need to be able to communicate fluently and professionally in written and spoken English.
Register for this course and see more details by visiting:
OpenCourser.com/course/u7gige/adversarial
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What's inside
Syllabus
Extend classical search to adversarial domains, to build agents that make good decisions without any human intervention—such as the DeepMind AlphaGo agent.
Read more
Syllabus
Extend classical search to adversarial domains, to build agents that make good decisions without any human intervention—such as the DeepMind AlphaGo agent.
Read more
Search in multi-agent domains, using the Minimax theorem to solve adversarial problems and build agents that make better decisions than humans.
Some of the limitations of minimax search and introduces optimizations & changes that make it practical in more complex domains.
Build agents that make good decisions without any human intervention—such as the DeepMind AlphaGo agent.
Extensions to minimax search to support more than two players and non-deterministic domains.
Introduce Monte Carlo Tree Search, a highly-successful search technique in game domains, along with a reading list for other advanced adversarial search topics.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Designed specifically for individuals with a foundation in search algorithms, linear algebra, and object-oriented Python
Offers practical insights into multi-agent adversarial domains, equipping learners to develop agents that outperform humans in decision-making
Taught by Thad Starner, a renowned expert in human-computer interaction and wearable computing
Provides a solid foundation for advanced adversarial search techniques, such as Monte Carlo Tree Search
Leverages real-world examples, such as the DeepMind AlphaGo agent, to illustrate the practical applications of adversarial search
Save this course
Save Adversarial Search to your list so you can find it easily later:
Save
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Activities
Be better prepared
before
your course. Deepen your understanding
during
and
after
it. Supplement your coursework and achieve mastery of the topics covered
in Adversarial Search with these
activities:
The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World
Show steps
This book offers a comprehensive overview of the field of machine learning and its potential impact on various aspects of our lives.
View
The Master Algorithm: How the Quest for the...
on Amazon
Show steps
-
Read through the chapters and make notes of key concepts
-
Research and explore topics that particularly interest you
Review Adversarial Search Algorithm Theory
Show steps
Revisiting the theoretical foundations of adversarial search algorithms will help you better understand their inner workings and limitations.
Browse courses on
Adversarial Search
Show steps
-
Go through your course notes or textbooks
-
Look for online resources and videos
Mentor Beginner Students
Show steps
Passing on knowledge to novice students not only aids them in their comprehension but also solidifies your own understanding of adversarial algorithms.
Show steps
-
Connect with beginner students in online forums or study groups
-
Offer assistance and guidance on topics related to adversarial algorithms as they arise
-
Provide constructive feedback and encouragement
Six other activities
Expand to see all activities and additional details
Show all nine activities
Review: Linear Algebra and Its Applications
Show steps
The book can help refresh your knowledge of linear algebra, which is a key component in several foundational topics in artificial intelligence.
View
Linear Algebra for Everyone
on Amazon
Show steps
-
Begin reading the book's first seven chapters
-
Complete the end-of-chapter exercises for these chapters
Write a Summary of the Course
Show steps
Summarizing the course material helps enhance retention, comprehension, and provides a valuable resource for future reference.
Show steps
-
Review course notes, assignments, and any other relevant materials
-
Identify key concepts, topics, and ideas
-
Organize and outline the material
-
Write a concise and coherent summary
Conduct Minimax Algorithm Practice
Show steps
Drills will help strengthen your understanding on designing and implementing minimax algorithm.
Browse courses on
Adversarial Search
Show steps
-
Find or create datasets for adversarial search
-
Implement a simple minimax algorithm to solve the adversarial search problem
Exercises on minimax algorithm
Show steps
Following these tutorials will help you gain hands-on practice with the Minimax algorithm and its applications.
Browse courses on
Game Theory
Show steps
-
Look for online tutorials and resources on minimax algorithm
-
Follow along with the tutorials and apply the algorithm to real-world examples
-
Implement minimax algorithm in a programming language
Attend a Workshop on Monte Carlo Tree Search
Show steps
Attending a workshop will provide you with structured guidance, hands-on practice.
Browse courses on
Monte Carlo Tree Search
Show steps
-
Find and register for a workshop on Monte Carlo Tree Search
-
Attend the workshop and actively participate in discussions and exercises
-
Apply what you learned in the workshop to your own projects or research
Implement Alpha-Beta Pruning Algorithm
Show steps
Developing an Alpha-Beta Pruning algorithm from scratch will challenge your comprehension and solidify knowledge.
Browse courses on
Optimization
Show steps
-
Understand the concept of alpha-beta pruning
-
Design and develop an algorithm
-
Test and refine your implementation
The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World
Show steps
This book offers a comprehensive overview of the field of machine learning and its potential impact on various aspects of our lives.
View
The Master Algorithm: How the Quest for the...
on Amazon
Show steps
Show steps
Show steps
- Read through the chapters and make notes of key concepts
- Research and explore topics that particularly interest you
Review Adversarial Search Algorithm Theory
Show steps
Revisiting the theoretical foundations of adversarial search algorithms will help you better understand their inner workings and limitations.
Browse courses on
Adversarial Search
Show steps
Show steps
Show steps
- Go through your course notes or textbooks
- Look for online resources and videos
Mentor Beginner Students
Show steps
Passing on knowledge to novice students not only aids them in their comprehension but also solidifies your own understanding of adversarial algorithms.
Show steps
Show steps
Show steps
- Connect with beginner students in online forums or study groups
- Offer assistance and guidance on topics related to adversarial algorithms as they arise
- Provide constructive feedback and encouragement
Six other activities
Expand to see all activities and additional details
Show all nine activities
Review: Linear Algebra and Its Applications
Show steps
The book can help refresh your knowledge of linear algebra, which is a key component in several foundational topics in artificial intelligence.
View
Linear Algebra for Everyone
on Amazon
Show steps
Show steps
Show steps
- Begin reading the book's first seven chapters
- Complete the end-of-chapter exercises for these chapters
Write a Summary of the Course
Show steps
Summarizing the course material helps enhance retention, comprehension, and provides a valuable resource for future reference.
Show steps
Show steps
Show steps
- Review course notes, assignments, and any other relevant materials
- Identify key concepts, topics, and ideas
- Organize and outline the material
- Write a concise and coherent summary
Conduct Minimax Algorithm Practice
Show steps
Drills will help strengthen your understanding on designing and implementing minimax algorithm.
Browse courses on
Adversarial Search
Show steps
Show steps
Show steps
- Find or create datasets for adversarial search
- Implement a simple minimax algorithm to solve the adversarial search problem
Exercises on minimax algorithm
Show steps
Following these tutorials will help you gain hands-on practice with the Minimax algorithm and its applications.
Browse courses on
Game Theory
Show steps
Show steps
Show steps
- Look for online tutorials and resources on minimax algorithm
- Follow along with the tutorials and apply the algorithm to real-world examples
- Implement minimax algorithm in a programming language
Attend a Workshop on Monte Carlo Tree Search
Show steps
Attending a workshop will provide you with structured guidance, hands-on practice.
Browse courses on
Monte Carlo Tree Search
Show steps
Show steps
Show steps
- Find and register for a workshop on Monte Carlo Tree Search
- Attend the workshop and actively participate in discussions and exercises
- Apply what you learned in the workshop to your own projects or research
Implement Alpha-Beta Pruning Algorithm
Show steps
Developing an Alpha-Beta Pruning algorithm from scratch will challenge your comprehension and solidify knowledge.
Browse courses on
Optimization
Show steps
Show steps
Show steps
- Understand the concept of alpha-beta pruning
- Design and develop an algorithm
- Test and refine your implementation
Career center
Learners who complete Adversarial Search will develop knowledge and skills
that may be useful to these careers:
Game Developer
Game Developer
Game Developers design, develop, and test video games. This course on adversarial search is highly relevant to game development, as it teaches you how to create intelligent agents that can make strategic decisions in game environments. By mastering these techniques, you can contribute to the creation of more engaging and challenging games.
Robotics Engineer
Robotics Engineer
Robotics Engineers design, build, and maintain robots. This course covers adversarial search, a crucial concept for developing robots that can navigate complex and dynamic environments. By learning how to build agents that make good decisions in adversarial settings, you'll gain an advantage in the field of Robotics Engineering.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use mathematical and statistical models to analyze financial data. This course on adversarial search provides essential skills for Quantitative Analysts, as it teaches how to build models that can make optimal decisions in adversarial markets. By mastering these techniques, you'll gain a competitive edge in the field of Quantitative Analysis.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts use mathematical and analytical techniques to solve complex business problems. This course on adversarial search provides valuable knowledge for Operations Research Analysts, as it teaches how to model and analyze multi-agent systems and make optimal decisions in competitive environments. By taking this course, you'll enhance your skillset and become a more effective Operations Research Analyst.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers design, build, test, and deploy machine learning models. This course explores adversarial search, allowing you to build agents that can make decisions without human intervention. These models can be applied to various domains, such as natural language processing, computer vision, and robotics. By taking this course, you'll gain a competitive edge in the rapidly growing field of Machine Learning Engineering.
Data Scientist
Data Scientist
Data Scientists analyze large amounts of data to extract insights and patterns. This course delves into adversarial search techniques, empowering you to create models that can handle multi-agent and adversarial scenarios. This knowledge is highly sought after in the data science industry, where the ability to make informed decisions based on complex data is essential. Consider taking this course to enhance your skillset as a Data Scientist.
Software Engineer
Software Engineer
Software Engineers design, develop, test, and maintain software systems. This course on adversarial search provides valuable knowledge for Software Engineers working on projects involving multi-agent systems or decision-making under uncertainty. By understanding adversarial search techniques, you can create more robust and intelligent software solutions.
Product Manager
Product Manager
Product Managers oversee the development and launch of new products. This course on adversarial search can provide valuable insights for Product Managers, as it teaches how to analyze competitive markets and develop products that meet customer needs. By understanding adversarial search techniques, you'll gain a competitive edge in the field of Product Management.
Business Analyst
Business Analyst
Business Analysts provide insights and recommendations to help businesses make informed decisions. This course on adversarial search is beneficial for Business Analysts, as it teaches how to analyze competitive environments and develop strategies that can lead to success. By understanding adversarial search techniques, you'll become a more valuable asset to any business.
Artificial Intelligence Researcher
Artificial Intelligence Researcher
Artificial Intelligence Researchers search for innovative ways of enabling computers to learn, reason, and act without explicit programming. This course dives deep into adversarial search, extending your search abilities to multi-agent or adversarial domains. By understanding how to approach adversarial problems, you can use game theory's minimax theorem to build agents that make better decisions than humans. This course may be useful for those interested in a career as an Artificial Intelligence Researcher.
Management Consultant
Management Consultant
Management Consultants provide advice and guidance to businesses on how to improve their performance. This course on adversarial search can be beneficial for Management Consultants, as it teaches how to analyze complex business environments and develop strategies that can lead to success. By understanding adversarial search techniques, you'll become a more effective Management Consultant.
Sales Manager
Sales Manager
Sales Managers lead and motivate sales teams to achieve revenue targets. This course on adversarial search may be useful for Sales Managers, as it teaches how to analyze competitive markets and develop strategies that can close deals. By understanding adversarial search techniques, you'll become a more effective Sales Manager.
Marketing Manager
Marketing Manager
Marketing Managers develop and execute marketing campaigns to promote products or services. This course on adversarial search may be useful for Marketing Managers, as it teaches how to analyze competitive markets and develop strategies that can reach target audiences. By understanding adversarial search techniques, you'll become a more effective Marketing Manager.
Financial Analyst
Financial Analyst
Financial Analysts provide insights and recommendations on investment opportunities. This course on adversarial search may be useful for Financial Analysts, as it teaches how to analyze competitive markets and identify undervalued assets. By understanding adversarial search techniques, you'll gain a competitive edge in the field of Financial Analysis.
Data Analyst
Data Analyst
Data Analysts collect, analyze, and interpret data to provide insights for businesses. This course on adversarial search may be useful for Data Analysts, as it teaches how to analyze competitive environments and identify trends that can impact business decisions. By understanding adversarial search techniques, you'll become a more valuable asset to any data-driven organization.
For more career information including salaries, visit:
OpenCourser.com/course/u7gige/adversarial
Reading list
We've selected 11 books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
Adversarial Search.
Save
This textbook provides a comprehensive overview of multiagent systems, including algorithms for solving adversarial problems. It valuable resource for anyone interested in learning more about the mathematical foundations of multiagent systems.
This textbook provides a comprehensive overview of reinforcement learning, a machine learning technique that can be used to solve adversarial problems. It valuable resource for anyone interested in learning more about the mathematical foundations of reinforcement learning.
Save
This textbook provides a comprehensive overview of deep learning, a machine learning technique that can be used to solve adversarial problems. It valuable resource for anyone interested in learning more about the mathematical foundations of deep learning.
Save
Provides a comprehensive overview of computer Go, a subfield of artificial intelligence that focuses on developing computer programs that can play the game of Go. It valuable resource for anyone interested in learning more about the history, algorithms, and techniques used in computer Go.
Save
Provides a comprehensive overview of the game of Go, including its history, rules, and strategies. It valuable resource for anyone interested in learning more about the basics of Go.
Save
This textbook provides a comprehensive overview of game theory, with a focus on applications to economics and politics. It valuable resource for anyone interested in learning more about the practical applications of game theory.
This ancient Chinese military treatise provides insights into the principles of strategy and warfare. It valuable resource for anyone interested in learning more about the philosophical foundations of adversarial search.
This classic Prussian military treatise provides insights into the principles of strategy and warfare. It valuable resource for anyone interested in learning more about the philosophical foundations of adversarial search.
Save
This classic Italian political treatise provides insights into the principles of strategy and power. It valuable resource for anyone interested in learning more about the philosophical foundations of adversarial search.
This classic work on the philosophy of science provides insights into the principles of logic and reasoning. It valuable resource for anyone interested in learning more about the philosophical foundations of adversarial search.
Save
This contemporary work on the philosophy of mind provides insights into the principles of thought and consciousness. It valuable resource for anyone interested in learning more about the philosophical foundations of adversarial search.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/u7gige/adversarial
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Effort
4 weeks
Via
Udacity
Instructor
Thad Starner
Language
English
Good to know
Designed specifically for individuals with a foundation in search algorithms, linear algebra, and object-oriented Python
Offers practical insights into multi-agent adversarial domains, equipping learners to develop agents that outperform humans in decision-making
Taught by Thad Starner, a renowned expert in human-computer interaction and wearable computing
Provides a solid foundation for advanced adversarial search techniques, such as Monte Carlo Tree Search
Leverages real-world examples, such as the DeepMind AlphaGo agent, to illustrate the practical applications of adversarial search
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