MAFS 5440: AI in Fintech

PKU

MAFS 5440. Artificial Intelligence in Fintech
Fall 2024

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, Lecture Theatre G (LTG)

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. CAO, He and Ms. LIU, Xuantong < aifin.hkust (add "AT gmail DOT com" afterwards) >

Schedule

Date	Topic	Instructor	Scriber
04/09/2024, Wed	Lecture 01: Overview and History of Artificial Intelligence in Fintech. [ slides A ][ slides B ] [ Project 1 ] Warm-up project description [ pdf ] Kaggle: Home Credit Default Risk [ link ]	Y.Y.
11/09/2024, Wed	Lecture 02: Supervised Learning: Linear Regression and Classification [ slides ] [ Reference ] To view .ipynb files below, you may try [ Jupyter NBViewer] Linear Regression Python Notebook [ MAFS6010_Regression.ipynb ] Linear Classification Python Notebook [ MAFS6010_Classification.ipynb ]	Y.Y.
25/09/2024, Wed	Lecture 03: Model Assessment and Selection: Subset, Ridge, Lasso, and PCR [ slides ] [Reference]: To view .ipynb files below, you may try [ Jupyter NBViewer] Python Notebook for Model Selection (Subset, Ridge, Lasso, and Principal Component Regression) [ MAFS6010_Selection.ipynb ]	Y.Y.
29/09/2024, Sun	Lecture 04: Decision Tree, Bagging, Random Forests and Boosting [ slides ] [Reference]: To view .ipynb files below, you may try [ Jupyter NBViewer] Python Notebook for Decision Trees, Bagging, Random Forests and Boosting [ MAFS6010_tree.ipynb ]	Y.Y.
02/10/2024, Wed	Lecture 05: Support Vector Machines [ slides ] [ Reference ]: To view .ipynb files below, you may try [ Jupyter NBViewer] Python Notebook for Support Vector Machines [ MAFS6010_svm.ipynb ]	Y.Y.
06/10/2024, Sun	Lecture 06: An Introduction to Convolutional Neural Networks [ slides ] [Reference]: To view .ipynb files below, you may try [ Jupyter NBViewer] LeNet5 for MNIST dataset in Pytorch Notebook [ LeNet5_mnist.ipynb ] LeNet5 for Cifar10 dataset in Pytorch Notebook [ LeNet5_cifar10.ipynb ] AlexNet for Cifar10 dataset in Pytorch Notebook [ AlexNet_cifar10.ipynb ] ResNet for Cifar10 dataset in Pytorch Notebook [ ResNet_cifar10.ipynb ] [ Topics on CNNs ]: Visualizing CNNs, transfer learning, neural style and adversarial examples [ slides ] To view .ipynb files below, you may try [ Jupyter NBViewer] Visualization of VGG16 in Pytorch Notebook [ vgg16-visualization.ipynb ] Class activation heatmap of VGG16 in Pytorch Notebook [ vgg16-heatmap.ipynb ] Fine-tuning (transfer learning) of ResNet in Pytorch Notebook [ finetuning_resnet.ipynb ] Neural Style of HKUST at Starry Night in Pytorch Notebook [ neural_style_starry-hkust.ipynb ] Adversarial examples of LeNet5 with MNIST [ LeNet5_mnist_fgsm.ipynb ]	Y.Y.
09/10/2024, Wed	Lecture 07: Other nonlinear models moving beyond linearity [ slides ] [Reference]: To view .ipynb files below, you may try [ Jupyter NBViewer] Python Notebook for ISLR Chapter 7 Lab [Ch7_Lab.ipynb ] [ Project 2 ] Project description: paper replication study [ pdf ] Asset Pricing paper replication [ pdf ] Reimaging paper replication [ pdf ] Shihao Gu, Bryan Kelly and Dacheng Xiu "Empirical Asset Pricing via Machine Learning", Review of Financial Studies, Vol. 33, Issue 5, (2020), 2223-2273. Winner of the 2018 Swiss Finance Institute Outstanding Paper Award. [ link ] Jingwen Jiang, Bryan Kelly and Dacheng Xiu "(Re-)Imag(in)ing Price Trends", Chicago Booth Report, Aug 2021 [ link ]	Y.Y.
16/10/2023, Wed	Lecture 08: An Introduction to Recurrent Neural Networks (RNN) and Long Short Term Memory (LSTM) [ slides ] [Reference]: To view .ipynb files below, you may try [ Jupyter NBViewer] Character-level RNN, LSTM and GRU for Name Classification [ char_rnn_classification_tutorial.ipynb ] RNN for generating Shakespeare's Sonnet [ rnn.ipynb ] [ shakespeare.txt ] LSTM for generating Shakespeare's Sonnet [ rnn_lstm_shakespeare.ipynb ] Generating Shakespeare's Sonnet: RNN, LSTM, Bidirectional LSTM, and Momentum-LSTM [ rnn_lstm_BiLSTM_mlstm_shakespeare.ipynb ] [ rnn_lstm_biLSTM_shakespeare.ipynb ] [ shakespeare.txt ] Bidirectional RNN for MNIST in pytorch [ bidirection_lstm_mnist.ipynb ] [ Youtube ] Nobel Prize in Physics 2024 Jorgen Schmidhuber's Critique: The #NobelPrizeinPhysics2024 for Hopfield & Hinton rewards plagiarism and incorrect attribution in computer science. It's mostly about Amari's "Hopfield network" and the "Boltzmann Machine." 1. The Lenz-Ising recurrent architecture with neuron-like elements was published in… — Jürgen Schmidhuber (@SchmidhuberAI) October 9, 2024 Geoff Hinton's response to Schmidhuber's critique: Geoff Hinton's response to Schmidhuber's critique on r/ML https://t.co/kUIznzbRBh https://t.co/joNcuzJnFk pic.twitter.com/3HWbfT5T9F — hardmaru (@hardmaru) April 23, 2020 Jorgen Schmidhuber: [ Deep Learning: Our Miraculous Year 1990-1991 ] [ Critique of Honda Prize for Dr. Hinton ]	Y.Y.
23/10/2024, Wed	Lecture 09: Attention and Transformers [ slides ] [Reference]: To view .ipynb files below, you may try [ Jupyter NBViewer] Pytorch Twitter Sentiment Analysis: RNN, LSTM, BiLSTM, Multihead Self-Attention. [ RNN (ipynb) ] [ LSTM (ipynb) ] [ BiLSTM (ipynb) ] [ BiLSTM with Multihead Attention (ipynb) ] [ BERT embedding with BiLSTM (ipynb) ] [ Sentiment140 dataset ] Pytorch Sentiment Analysis with IMDB data: RNN, (bi)-LSTM, CNN, Transformer, BERT, etc. [ GitHub ] Illustrated Transformer by Jay Alammar: [ link ] The Annotated Transformer Tutorial by Sasha Rush: [ link ] BERT generation of Shakespeare's sonnet: [ BERT_shakespeare_gen.ipynb ] BERT next sentence generation of Shakespeare's sonnet and Chinese poems: [ BERT_shakespeare_nextsen.ipynb ] Chinese BERT (Whole-Word-Masking): [ link ] [ Guest Talk ] Title: Sharing from Super Quantum Fund Speaker: Invited guests from Super Quantum Introduction: Super Quantum is a quantitative asset management firm founded in 2019 by Professor Michael Zhang. With a vision to bring ‘genuine’ science to investing in the opportunistic China-A share market, the team combines state-of-the-art research on deep learning, mathematics, econometrics and statistics, with solid experience in the finance industry to design quantitative strategies that consistently outperform the market. Super Quantum currently manages 5-10 billion RMB and has Type 9 (asset management) license from SFC in Hong Kong.	Y.Y.
30/10/2024, Wed	Lecture 10: Transformer and Applications [ slides ] [Reference]: To view .ipynb files below, you may try [ Jupyter NBViewer] Pytorch Twitter Sentiment Analysis: RNN, LSTM, BiLSTM, Multihead Self-Attention. [ RNN (ipynb) ] [ LSTM (ipynb) ] [ BiLSTM (ipynb) ] [ BiLSTM with Multihead Attention (ipynb) ] [ BERT embedding with BiLSTM (ipynb) ] [ Sentiment140 dataset ] Pytorch Sentiment Analysis with IMDB data: RNN, (bi)-LSTM, CNN, Transformer, BERT, etc. [ GitHub ] Illustrated Transformer by Jay Alammar: [ link ] The Annotated Transformer Tutorial by Sasha Rush: [ link ] BERT generation of Shakespeare's sonnet: [ BERT_shakespeare_gen.ipynb ] BERT next sentence generation of Shakespeare's sonnet and Chinese poems: [ BERT_shakespeare_nextsen.ipynb ] Chinese BERT (Whole-Word-Masking): [ link ]	Y.Y.
06/11/2024, Wed	Lecture 11: An Introduction to Reinforcement Learning with Applications in Quantitative Finance [ slides ] [ Reference ]: Google DeepMind's Deep Q-learning playing Atari Breakout: [ youtube ] To view .ipynb files below, you may try [ Jupyter NBViewer] Deep Q-Learning Pytorch Tutorial: [ link ] A Tutorial of Reinforcement Learning for Quantitative Trading: [ Tutorial ] [ Replicate ] FinRL: Deep Reinforcement Learning for Quantitative Finance [ GitHub ] Reinforcement Learning and Supervised Learning for Quantitative Finance: [ link ] Hierarchical Reinforced Trader (HRT): A Bi-Level Approach for Optimizing Stock Selection and Execution, by Zijie Zhao, Roy E. Welsch. [ arXiv:2410.14927 ] Prof. Michael Kearns, University of Pennsyvania, Algorithmic Trading and Machine Learning, Simons Institute at Berkeley [ link ]	Y.Y.
13/11/2024, Wed	Lecture 12: Final Project. [ Project 3 ] Description: [ project3.pdf ] Kaggle: Jane Street Forecasting [ slides ] Kaggle: M5 Contests [ slides ] Cryptocurrency Trading Project [ slides (pdf) ] Large Language Models with Financial Analysis [ slides (pdf) ] [ Reference ] Kaggle: Jane Street Forecasting. [ link ] Kaggle: M5 Forecasting - Accuracy, Estimate the unit sales of Walmart retail goods. [ link ] Kaggle: M5 Forecasting - Uncertainty, Estimate the uncertainty distribution of Walmart unit sales. [ link ] [ Reference: Guest Talk ] Title: Sequential Predictive Conformal Inference for Time Series [ slides ] Speaker: Chen XU, Georgia Institute of Technology Abstract: We present a new distribution-free conformal prediction algorithm for sequential data (e.g., time series), called the sequential predictive conformal inference (SPCI). We specifically account for the nature that time series data are non-exchangeable, and thus many existing conformal prediction algorithms are not applicable. The main idea is to adaptively re-estimate the conditional quantile of non-conformity scores (e.g., prediction residuals), upon exploiting the temporal dependence among them. More precisely, we cast the problem of conformal prediction interval as predicting the quantile of a future residual, given a user-specified point prediction algorithm. Theoretically, we establish asymptotic valid conditional coverage upon extending consistency analyses in quantile regression. Using simulation and real-data experiments, we demonstrate a significant reduction in interval width of SPCI compared to other existing methods under the desired empirical coverage. Source codes can be found at [ GitHub ]. Bio: Chen Xu is currently a 4th year Operations Research PhD at Georgia Tech ISyE, where he is supervised by Prof. Yao Xie. His current research interests are two-fold. (1) Uncertainty quantification for machine learning models. Specifically, advance conformal prediction as a distribution-free method for arbitrarily complex deep models, especially in the context of time-series modeling. (2) Generative models through flow-based neural networks. Specifically, develop scalable computational tools for problems at the intersection of statistics and optimization, including extensions to high-dimensional optimal transport, distributionally robust optimization, and differential privacy. He has published in top machine learning conferences (e.g., ICML 2021 oral, NeurIPS 2023 spotlight) and journals (e.g., IEEE TPAMI 2023, IEEE JSAIT).	Y.Y.

by YAO, Yuan.