MAFS6010U: AI in Finance

PKU

MAFS6010U. Artificial Intelligence in Finance
Spring 2020

Course Information

Synopsis

This course explores the basic concepts and underlying principles of artificial intelligence (AI), delving into the fundamentals of machine learning with insights from case studies of relevant technologies. Allowing for the experimentation of applications of machine learning, this course is designed to encourage students to devise creative ways to put readily-available AI technologies to use to tackle problems in real life. With a focus on the conceptual understanding of the fundamentals of AI, the purpose of this course is two-fold:

1. To educate the next generation of innovators of the digital economy, especially in the financial sector;

2. To equip FinTech leaders with the conceptual frameworks and analytical tools for the development and deployment of AI-enabled applications and technologies.

Prerequisite: Some preliminary course on (statistical) machine learning, applied statistics, and deep learning will be helpful.

Instructors:

Anthony Woo [ Resume ]

Yuan Yao

Time and Place:

Wednesday 15:00-17:50, Zoom online, HKUST

Reference (参考教材)

An Introduction to Statistical Learning, with applications in R (ISLR). 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.

Homework and Projects:

TBA (To Be Announced)

Teaching Assistant:

Email: Mr. Weizhi ZHU aifin.hkust (add "AT gmail DOT com" afterwards)

Schedule

Date	Topic	Instructor	Scriber
25/02/2020, Tue	Lecture 01: History and Overview of Artificial Intelligence. [ slides ]	A.W. and Y.Y.
03/03/2020, Tue	Lecture 02: Supervised Learning: Linear Regression and Classification [ YY's slides ] [ Topic ] Google Experiments: Vision Sensing and Case study: HireVue (Video Analytics for Recruitment)[ AW's slides ] [ Reference ] To view .ipynb files below, you may try [ Jupyter NBViewer] Linear Regression Python Notebook [ MAFS6010u_Regression.ipynb ] Linear Classification Python Notebook [ MAFS6010u_Classification.ipynb ]	A.W. Y.Y.
10/03/2020, Tue	Lecture 03: Model Assessment and Selection: Subset, Ridge, Lasso, and PCR [ YY's slides ] [Reference]: To view .ipynb files below, you may try [ Jupyter NBViewer] Python Notebook for Model Selection (Subset, Ridge, Lasso, and Principal Component Regression) [ MAFS6010u_Selection.ipynb ] [ Topic ]: [ AW's slides ] Poll results on "Beyond the Classroom" Symbolic vs. Connectionist AI Group formation and logistics Case study: WorkFusion Best practices of CV writing and interview preparation	Y.Y. A.W.
17/03/2020, Tue	Lecture 04: Decision Tree, Bagging, Random Forests and Boosting [ YY's slides ] [Reference]: To view .ipynb files below, you may try [ Jupyter NBViewer] Python Notebook for Decision Trees, Bagging, Random Forests and Boosting [ MAFS6010u_tree.ipynb ] [ Topic ]: Some Kaggle Challenges for Project Option [ Weizhi ZHU's slides ] [ Topic ]: [ AW's slides ] Group project Q&A Monthly Article Template: First batch due on March 31 Banking and Markets Case Study: QxBranch (Quantum Computing) Case Study: Prowler (General AI) Interview Preparation: Investment Banking Essentials	Y.Y.
24/03/2020, Tue	Lecture 05: Support Vector Machines [ YY's slides ] [Reference]: To view .ipynb files below, you may try [ Jupyter NBViewer] Python Notebook for Support Vector Machines [ MAFS6010u_svm.ipynb ] [ Topic ]: Assignment II and Impacts on Global Markets etc. [ AW's slides ]	Y.Y.
03/31/2020, Tue	Lecture 06: A Tutorial on Applications of Machine Learning: Pair Trading. [Reference]: Weizhi ZHU: [ PairTrading-1.ipynb ] [ PairTrading-2.ipynb ] [ Topic ]: Anthony Woo's talk [ AW's slides ] Global Markets Overview (Cont'd) Case Study on Prowler (Cont'd) Case Study on QxBranch & Rigetti Best Practices of CV Writing	Weizhi ZHU; Anthony Woo
04/07/2019, Tue	Lecture 07: An Introduction to Convolutional Neural Networks [ YY's 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 ] [ Invited Talk ] Speaker: FONG Katrina, Fintech Recruitment [ slides ]	A.W. Y.Y.
04/14/2019, Tue	Lecture 08: Topics in CNN: Visualization, Transfer Learning, Neural Style, and Adversarial Examples [ YY's slides ] [Reference]: 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 ] [ Topic ]: Speaker: Anthony Woo [ slides ]	A.W. Y.Y.
04/21/2019, Tue	Lecture 09: An Introduction to Unsupervised Learning: PCA, AutoEncoder, VAE, and GANs [ YY's slides ] [Reference]: To view .ipynb files below, you may try [ Jupyter NBViewer] DCGAN for MNIST Tutorial in Pytorch Notebook [ dcgan_mnist_tutorial.ipynb ]	A.W. Y.Y.
04/28/2020, Tue	Lecture 10: An Introduction to Recurrent Neural Networks (RNN) and Long Short Term Memory (LSTM) [ YY's 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 Poems [ rnn.ipynb ] LSTM for generating Shakespeare's Poems [ lstm.ipynb ] [ Topic ] [ AW's slides ] [ Deep Instinct: Reinventing Cybersecurity Prevention with Deep Learning ]	A.W. Y.Y.
05/05/2020, Tue	Lecture 11: Attention, Transformer and BERT [ YY's slides ] [Reference]: To view .ipynb files below, you may try [ Jupyter NBViewer] 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 ] [ Topic ] [ AW's slides ] [ The Rise of AI-Powered Company in the Postcrisis World ]	A.W. Y.Y.
05/12/2020, Tue	Lecture 12: An Introduction to Reinforcement Learning [ YY's slides ] [ Reference ]: Prof. Michael Kearns, University of Pennsyvania, Algorithmic Trading and Machine Learning, Simons Institute at Berkeley [ link ] To view .ipynb files below, you may try [ Jupyter NBViewer] Deep Q-Learning Pytorch Tutorial: [ link ] Reinforcement Learning and Supervised Learning for Quantitative Finance: [ link ] Quantum Computing at IBM: [ link ] [ Topic ] Speaker: Dr. Michael Chen, Former Executive Director at Harvard Center Shanghai.	A.W. Y.Y.
05/19/2020, Tue	Lecture 13: Tutorial on Reinforcement Learning in Quantitative Trading [ Weizhi's slides ] [ Reference ]: To view .ipynb files below, you may try [ Jupyter NBViewer] Python Notebook on Deep Q-Learning in Trading: [ 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 ] Kaggle: Home Credit Default Risk [ link ] Kaggle: COVID-19 Open Research Dataset Challenge (CORD-19) [ link ] Quantapian: free education, data, and tools so anyone can pursue quantitative finance. [ link ] [ Topic ] Speakers: Brian Zheng and Anthony Woo [ slides ] [ Final Reports ] GitHub Repository for reports of Final Project [ GitHub ] [ Chatbot ] 1. LI Jiaqi, LIN Tuoyu, LIU Genghuadong, ZHANG Zehao, and ZHOU Quan. NLP Chatbot for CogX Website. [ report ] [ slides ] [ source (github) ] [ presentation (youtube) ] 2. LI, Liangde, Yaxin Zhang, Linfeng Zhu, Yuqiao Xie, and Qi Liu. Financial Chatbot Based on LSTM. [ report ] [ source (github) ] [ presentation (youtube) ] 3. XU Wenhao, LI Yihang, ZHANG Yanran, and LAI Kunyao. Chatbot Based on Prepared Answer Set. [ report ] [ source, presentation (baidu:5311) ] 4. CHEN Xiaoyu, SUN Lei, XU Yuqing, YIN Li, and ZHU Hanfeng. NLP Chatbot. [ report ] [ source (github) ] [ presentation (youtube) ] [ Kaggle ] 1. ZHANG Zhiyi, HU Zhenzhuo, and WU Siliang. M5 Forecasting with LightGBM. [ report ] [ slides ] [ source (github) ] [ presentation (bilibili) ] 2. ZHU, Changjun. Kaggle - M5 Forecasting - Accuracy. [ report ] [ source (github) ] [ presentation (bilibili) ] 3. Yinuo Wang, Tingting Jiang, Wenjun Li, Haolan Fan, Fangrui Zhang, Yijia Song, and Shihao Yang. M5 Forecasting Competition Report. [ report ] [ source (dropbox) ] [ presentation (google) ] 4. CAI Zhuoheng, HAN Yan, LI Lingpan, and LI Yiling. Wal-Mart Sales Prediction using XGBoost Algorithm. [ report ] [ source (github) ] [ presentation (bilibili) ] 5. DO, Van Thuat. Home Credit Default Risk. [ report ] [ source (github) ] [ presentation (google) ] 6. LAO Shengbo, SHI Kai, TANG Haokai, and ZHOU Zhixuan. Estimate the Unit Sales of Walmart Retail Goods in the USA. [ report ] [ source (github) ] [ presentation (google) ] 7. ZHONG, Jing and LIU, Ruiyan and LI, Han and LI, Yijin and ZHANG, Xuanyu and ZHANG, Tianyu. TIME SERIES PREDICTION ON SALES OF WALMART. [ report ] [ source (github) ] [ presentation (youtube) ] 8. WANG Su, WANG Boyu, ZHAO Yinuo, GAO Yuxin, and LI Qian. Estimate the unit sales of Walmart retail goods. [ report ] [ source (dropbox) ] [ presentation (bilibili) ] [ Quantopian ] 1. LI, Fanyi, DU Yushu, HUANG Jian, TONG Jiaqu, and YU Rong. Project of Artificial Intelligence in Finance. [ report ] [ slides ] [ source (github) ] [ presentation (youtube) ] 2. Nan Xi; Zhao Yanan; Shi Xinrun; Ji chunhua. Smart Beta Trading Strategy Based on Fundamental Factors. [ report ] [ source (github) ] [ presentation (youtube) ]	Weizhi ZHU A.W. Y.Y.

by YAO, Yuan.