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[Lecture] Artificial Intelligence in Economics and Finance
Sep. 13, 2022


Speaker: Prof. Serguei Maliar, Santa Clara University

Host: Prof. Li Bo, School of Economics, Peking University

Time: 20:00-22:00 pm, September 13/15/17, 2022, GMT+8

Venue:

Scan the QR code to register and the deadline is September 9.



Timetable:

Day1:
1. Introduction to machine learning.
2. Supervised learning (linear and logistic regression). Multiclass classification
3. Multilayer neural networks. Deep learning.

Day2:
1. Unsupervised learning (clustering, dimensionality reduction).
2. Applications of unsupervised learning for economic dynamics.
3. ToTEM central banking model of the Bank of Canada for projection and policy analysis.

Day3:
1. Introduction to reinforcement learning
2. Reinforcement learning for solving large scale heterogeneous-agent models.
3. Examples of python and TensorFlow applications.

Abstract:

Artificial intelligence (AI) has many impressive applications, including playing chess and Go, self-driving cars, computer vision, and speech recognition. In this mini-course, we demonstrate that many challenging applications in economics, business and finance can be successfully analyzed by using the same break-ground AI technologies and the same state-of-the-art combinations of software and hardware as those used by data scientists for dealing with their impressive applications.

We develop open-source deep learning AI software that makes it possible to cast dynamic optimization problems into the form suitable for intelligent machines. We show how this software can be used to analyze a collection of high-dimensional economic applications that were intractable under the earlier solution methods.

The mini-course provides an introduction into three main subfields of modern data, which are science–supervised, unsupervised and reinforcement learning. In particular, we review techniques that are particularly useful in economics, business and finance including deep learning networks, stochastic optimization, clustering, dimensionality reduction, regularization, decision trees, ensembles and support vector machines. We describe the implementation of our AI solution framework using both Matlab and Python languages including the Google TensorFlow platform.

Source: Peking University and the World