PKU

MAFS 5440. Artificial Intelligence in Fintech
Fall 2025

Course Information

Synopsis

This course offers a comprehensive exploration of the fundamental concepts and underlying principles of artificial intelligence (AI). It delves into the core principles of machine learning and provides valuable insights through case studies of relevant technologies. By providing opportunities for hands-on experimentation with machine learning applications, the course aims to inspire students to devise innovative approaches to address real-life problems in fintech using readily-available AI technologies.
Prerequisite: Some preliminary course on (statistical) machine learning, applied statistics, and deep learning will be helpful.

Instructors:

Yuan Yao

Time and Place:

Wednesday 19:30-22:20pm, G010, CYT Bldg (140)

Reference (参考教材)

An Introduction to Statistical Learning, with applications in R / Python. By James, Witten, Hastie, and Tibshirani

ISLR-python, By Jordi Warmenhoven.

ISLR-Python: Labs and Applied, by Matt Caudill.

Manning: Deep Learning with Python, by Francois Chollet [GitHub source in Python 3.6 and Keras 2.0.8]

MIT: Deep Learning, by Ian Goodfellow, Yoshua Bengio, and Aaron Courville

Tutorials: preparation for beginners

Python-Numpy Tutorials by Justin Johnson

scikit-learn Tutorials: An Introduction of Machine Learning in Python

Jupyter Notebook Tutorials

PyTorch Tutorials

Deep Learning: Do-it-yourself with PyTorch, A course at ENS

Tensorflow Tutorials

MXNet Tutorials

Theano Tutorials

The Elements of Statistical Learning (ESL). 2nd Ed. By Hastie, Tibshirani, and Friedman

statlearning-notebooks, by Sujit Pal, Python implementations of the R labs for the StatLearning: Statistical Learning online course from Stanford taught by Profs Trevor Hastie and Rob Tibshirani.

Teaching Assistant:


Email: Mr. FU, Xiaoyi < aifin.hkust (add "AT gmail DOT com" afterwards) >

Schedule

Date Topic Instructor Scriber
03/09/2025, Wed Lecture 01: Overview and History of Artificial Intelligence in Fintech. [ slides ] Y.Y.
10/09/2025, Wed Lecture 02: Supervised Learning: Linear Regression and Classification [ slides ] Y.Y.
11/09/2025, Thu Seminar.
  • Title: PKU Quest: AI-Powered Math Education Practice at Peking University [ announcement ] [ slides ]
  • Speaker: Leheng Chen and Zihao Liu, Peking University
  • Time: Thursday Sep 11, 2025, 3:30pm
  • Venue: Room 2612B (near Lift 31 & 32)
  • Abstract: The advent of Generative AI necessitates a paradigm shift in higher education, calling for new, diverse models of interaction between students, teachers, and AI. In response to this challenge, Peking University has developed PKU Quest, an AI-assisted platform designed to explore these new pedagogical frontiers. PKU Quest focuses on optimizing for the unique demands of mathematics education, and has developed the "Math Tutor," a tool specifically designed for math problem-solving support. Instead of providing direct answers, the Math Tutor engages students in a heuristic and exploratory dialogue, guiding them to develop independent thinking and problem-solving skills. This application has now been implemented across all foundational mathematics courses at Peking University. This presentation will share our journey in developing PKU Quest, discussing the motivations, challenges, and practical outcomes of what we consider a first step in exploring the vast potential of AI in education.
  • Bio: Leheng Chen is a Ph.D. student at the Beijing International Center for Mathematical Research (BICMR), Peking University, advised by Professor Bin Dong. He has broad interests in the application of artificial intelligence. Previously, he explored research directions in AI for Science, such as thermodynamic modeling and foundation models for partial differential equations, with his work published in Physical Review E and at an ICLR Workshop. He has since shifted his research focus to the practical application of AI in Education, where he designed and developed "PKU Quest," an AI-assisted teaching and learning platform for Peking University.
    Zihao Liu (Leo) is a Ph.D. student in Applied Mathematics and Artificial Intelligence at the School of Mathematical Sciences, Peking University. His interests span the application of AI to education and scientific understanding, with recent work focusing on improving the pedagogical effectiveness of AI-powered educational agents and building benchmark datasets for evaluating AI capabilities. As the founder and lead developer of PKU Quest and AKIS (AI Knowledge Intelligent Solution), he focuses on the practical deployment of AI-in-education systems and has helped design and develop “AIBOOKS,” an intelligent digital-textbook platform, and “Math Tutor,” a guided problem-solving assistant for students. He is deeply committed to advancing the integration of AI and education.
Y.Y.
17/09/2025, Wed Lecture 03: Model Assessment and Selection: Subset, Ridge, Lasso, and PCR [ slides ]
    [ Seminar ]
  • Speaker: QRT guest speakers [ poster ]
 Y.Y.
by YAO, Yuan.