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Data Science Decisions in Time

Information Theory & Games

Thomas Woolf

This is part of our specialization on Making Decision in Time. For this third course we start with an intriguing study on SFPark and build new insights into the ideas that flow from this direction. The ending point should bring new code and new algorithm insights into perspective, and use, by many computer and data scientists.

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What's inside

Syllabus

Dynamically Changing Environments and Game Theory

How should a control be adjusted to best achieve a desired outcome? We introduce the SFPark problem, a real parking management approach being used in SF. The question that we want to understand, via sequential methods and games, is how best to set the prices for spaces, dynamically during the day, to encourage a particular (say 15%) free space availability. The game is between the consumers (looking for parking) and the city (trying to optimize space, reducing those cruising for spaces and encouraging those coming for a meal or for shopping to have a parking space). This is a sequential decision problem that can also be described as a game.

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Designed for students with an interest in computational social science

Guides learners in understanding how to make decisions in dynamic environments

Taught by Thomas Woolf, a recognized expert in decision-making theory

Examines real-world applications of game theory, including the SFPark parking management system

Covers advanced topics such as sequential social environments and avoiding distribution shifts

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Reviews summary

Theoretical depth in data science decisions

Learners say this course offers a deep dive into decision-making in data science, particularly focusing on information theory and game theory concepts. Many commend the instructor's profound knowledge and the challenging yet rewarding nature of the material, finding it excellent for those with a solid quantitative background. However, some indicate it is highly demanding and not suitable for beginners, with dense lectures and complex assignments that may require significant self-study to grasp fully.

Strong theoretical base, but some sought more hands-on coding or practical examples.

"The theory is solid, which is great, but I was hoping for more practical coding exercises to apply what I learned."

"I gained a deep theoretical understanding, which is what I wanted, but others might need more direct code demonstrations."

"While the SFPark case study was insightful, I wished for more diverse real-world implementations beyond the conceptual."

Instructor demonstrates deep expertise and passion for the subject.

"The professor clearly understands the material inside and out, making the lectures insightful and engaging."

"I found the instructor's explanations, while sometimes fast, to be very profound and thought-provoking."

"His experience in the field shone through, providing valuable context and real-world relevance to the theories."

Provides a rigorous, in-depth exploration of complex concepts.

"The course delves into advanced topics with mathematical rigor, offering a truly comprehensive understanding."

"I appreciated the comprehensive coverage of game theory applied to real-world problems and data science scenarios."

"It offers a strong conceptual foundation, moving beyond surface-level explanations into the core principles."

Assignments are complex and require significant independent problem-solving.

"The assignments were incredibly difficult, sometimes feeling disconnected from the lectures and requiring extensive self-study."

"I spent hours trying to decipher what was expected in some homework problems; more examples would have been helpful."

"While challenging, the problems really pushed my understanding, but I wished for more step-by-step guidance on harder tasks."

Highly challenging; best for learners with prior math/CS experience.

"This course is not for the faint of heart; I felt lost without a solid math and programming background."

"Prior knowledge in probability, linear algebra, and even some basic optimization is absolutely essential to keep up."

"If you are a beginner in data science or game theory, prepare for a steep learning curve and a lot of extra effort."

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 Data Science Decisions in Time: Information Theory & Games with these activities:

Review key concepts in game theory

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Brushing up on fundamental concepts will help you understand the more advanced topics covered in this course.

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Go over your lecture notes or textbook chapters on game theory.
Review online resources or articles to reinforce your understanding.
Solve practice problems to test your knowledge.

Compile a collection of resources on game theory and its applications

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Creating a compilation will provide you with a valuable reference and help you stay organized.

Browse courses on Online Courses

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Identify different types of resources (e.g., books, articles, videos).
Search for and gather relevant resources.
Organize and categorize the resources.
Consider creating an online repository or sharing your compilation with others.

Attend conferences or meetups focused on game theory or artificial intelligence

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Attending events will allow you to connect with professionals in the field, learn about current research, and stay up-to-date with industry trends.

Browse courses on Networking

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Identify relevant conferences or meetups in your area.
Register and attend the events.
Engage with speakers, attendees, and exhibitors.
Follow up with interesting contacts.

Four other activities

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Practice solving game theory problems

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Regular practice will strengthen your problem-solving skills and improve your ability to apply game theory concepts.

Browse courses on Dynamic programming

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Find practice problems online or in textbooks.
Start with easier problems and gradually increase the difficulty.
Review your solutions and identify areas for improvement.

Explore online tutorials on advanced game theory topics

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Following guided tutorials will introduce you to new concepts and techniques that can extend your knowledge.

Browse courses on Mechanism Design

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Identify specific topics or areas where you want to expand your knowledge.
Search for reputable online tutorials or courses on those topics.
Follow the tutorials and complete the exercises or assignments.

Develop a case study on a real-world application of game theory

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Creating a case study will deepen your understanding of game theory and its practical implications.

Browse courses on Real-World Examples

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Choose a specific real-world scenario that involves game theory.
Research and gather data on the scenario.
Analyze the scenario using game theory principles.
Develop recommendations based on your analysis.
Write up your case study and share it with others.

Contribute to open-source projects related to game theory or artificial intelligence

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Contributing to open-source projects will provide you with hands-on experience and deepen your understanding of the latest advancements.

Browse courses on AI Algorithms

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Find open-source projects that align with your interests and skill level.
Review the project documentation and identify areas where you can contribute.
Make code contributions, bug fixes, or documentation improvements.
Engage with the project community and learn from others.

Career center

Learners who complete Data Science Decisions in Time: Information Theory & Games will develop knowledge and skills that may be useful to these careers:

Reading list

We've selected 30 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 Data Science Decisions in Time: Information Theory & Games.

Elements of Information Theory

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Is widely regarded as the standard graduate-level textbook on information theory. It provides a comprehensive and rigorous treatment of all essential topics, including entropy, data compression, channel capacity, and error-correcting codes. It is an invaluable reference tool for students and professionals and is commonly used in academic institutions worldwide.

Elements of Information Theory 2nd Edition (Wiley...

Hardcover

$$$

Elements of Information Theory

Kindle Edition

$$$$

Information Theory, Inference and Learning...

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A modern classic that beautifully connects information theory with inference and machine learning. is highly recommended for those interested in the practical applications of information theory in areas like data science and artificial intelligence. It serves as an excellent textbook and reference, known for its clear explanations and broad coverage.

Information Theory, Inference and Learning...

Hardcover

$$$

Network Information Theory

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This comprehensive textbook focuses on the application of information theory to network communication. It covers both classical and recent results in the field, making it highly relevant for graduate students and researchers interested in modern communication systems. It provides a deep dive into topics like network coding and multi-terminal networks, building upon foundational information theory.

Network Information Theory

Hardcover

$$$$

Network Information Theory

Kindle Edition

$$$

Information Theory and Reliable Communication

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A classic textbook focusing on the fundamental mathematical models of sources and channels in communication systems and the framework for reliable communication. While published in 1968, its rigorous treatment of core concepts remains relevant. It valuable reference for those delving deeper into the theoretical underpinnings of error correction and communication system design.

Data Science Decisions in Time

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What's inside

Syllabus

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Reviews summary

Theoretical depth in data science decisions

Activities

Career center

Reading list

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