ECON 424: Machine Learning in Economics

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Be at the forefront of AI.

Artificial intelligence is the Internet’s newest frontier, and at UW, you have the opportunity to get ahead of the trend!

ECON 424: Machine Learning in Economics allows you to explore how AI can be used to predict different outcomes based on data sets about human behaviour.

This article will be your go-to guide for everything about ECON 424, including its learning takeaways, class structure, and the required readings and assignments. We’ll also hear from Dr. Mikko Packalen, who teaches the course. Read on to find out how UW is leading the pack in AI and economics!

About ECON 424: Machine Learning in Economics 

What you’ll learn 

In ECON 424, you’ll examine how machine learning algorithms work, what you can do with these algorithms, and how to use AI more broadly. While traditional economic methods focus on estimating causal effects, you’ll learn how to use AI to predict outcomes and how to use this alongside causal estimation. In addition, you’ll explore supervised and unsupervised learning, text analysis, regression trees, penalized regression, classification, random forest, neural network, and boosting methods. And the best part? You’re not only allowed to use Chat GPT and other AI programs in class, it’s a requirement! 

Prerequisites 

This course is an advanced level Economics course, meaning you’ll need sufficient academic progression and a solid understanding of economics. To take this course, you’ll have to be in an Honours program or be an Economics or Mathematical Economics major. You’ll also need to have completed either ECON 323: Econometric Analysis 2, STAT 221: Statistics (Non-Specialist Level), STAT 231: Statistics, or STAT 241: Statistics (Advanced Level).  

Major themes 

• Chatbots and artificial intelligence 

• In ECON 424, you’ll be expected to use AI to help with every aspect of your learning, including coding and building algorithms. You’ll have the opportunity to work with Chat GPT and other AI systems to create code, deepening your understanding of AI and how to prompt it for the right results. 

• Prediction 

• You’ll learn about the importance of prediction in relation to causal estimation. While the latter is important, predicting is sometimes what is needed at a certain time. You’ll get to better understand prediction, as well as how it works with causal estimation. 

• Applying machine algorithms 

  • Deep learning is algorithms that underlies big applications, like Chat GPT. You’ll explore how these algorithms work and apply them to sets of data to better understand AI. 

Readings 

The readings consist of supplementary material that will help you program the AI algorithms you will be creating. This includes a textbook that contains the instructions on creating Python code. However, if you are already familiar with coding and creating algorithms, you will likely not need to consult the textbook. You’ll also read from scientific journals about the content covered in the course that will extend your understanding about AI. 

Assignments 

Every week, you’ll be given a set of data and be required to create an algorithm that predicts an outcome. These predictions are centred around human behaviour and characteristics. You’ll use this data to build algorithms on an AI system like Chat GPT to predict future behaviour. In class, you’ll participate in prediction competitions where you’ll test your algorithm and see how accurate it is compared to those of your classmates. 

An average ECON 424 class 

Class begins with a discussion of recent developments in AI, which students are encouraged to follow in their time outside of class. Then, you’ll examine machine learning algorithms and their economic applications through listening to a lecture. Finally, you’ll discuss algorithms in class to prepare you for your weekly homework assignment. 

Why take this course? 

AI is the next big thing when it comes to technology, and this course provides the opportunity for students to get ahead of the game. For Economics majors, an understanding of algorithms and AI will be a crucial skill to possess in the job market. However, other majors and programs will find enjoyment in the mental workouts this class provides. Students will be challenged to think differently and learn not only more about AI, algorithms, and prediction, but also about themselves and perseverance. 

About Dr. Packalen 

Dr. Mikko Packalen researches the impact of new ideas, specifically how demographics, geography, and institutions influence the adoption of new ideas by scientists and inventors, how new ideas develop into transformative ideas that improve health outcomes and facilitate economic growth, and how to measure the novelty of science and invention. An accomplished writer, Dr. Packalen’s work has been published in newspapers including The Telegraph, Toronto Sun, and Financial Post. His current work has been recognized and has earned him funding from the National Institute of Aging and Amazon Web Services. 

Why do you like teaching this course?  

“Understanding machine learning and artificial intelligence are valued skills on the job market, and machine learning algorithms influence our choices and lives in so many ways through different digital platforms including Amazon, TikTok, Facebook, X,Netflix, and Google. As everyone can see from the news every day, machine learning and artificial intelligence are also at the cutting edge of civilization: every week we learn new things about the possibilities and limitations of machine learning and artificial intelligence. These make teaching and studying machine learning and artificial intelligence very exciting.” 

How do you engage students during class? 

“In each class we discuss recent developments in artificial intelligence – there is exciting news every day!  The course is also very applied: students learn new algorithms by applying them in weekly prediction competitions. Your grade depends in part on how accurate your predictions are relative to your classmates’ predictions. The competition is very friendly though, students cooperate with each other a lot.”   

What do you hope students will get out of this course? 

“I hope that taking the course changes the student’s learning trajectory. The course is successful if students spend more time on studying machine learning and AI after the course than they did before the course. When this happens, the impact of the course on student’s knowledge is permanent. I’m hoping that my passion for machine learning and AI is contagious and makes this permanent change in learning possible.” 

What is your favourite topic in this course? 

“I have a new favourite machine learning application every week! This week I talked to students about a brand new study in which economists used deep learning machine learning algorithms to examine who are the innovators in the future based on high school year-book photos.  Turns out there is more innovation when high school students display more style novelty. When we are more tolerant toward people who dress differently, people appear to feel more free to also think differently and innovate!”

Special thanks to Dr. Packalen for providing an interview for this article!