MATH4995: Capstone Project for Data Science

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 wzhuai (add "AT connect DOT ust DOT hk" afterwards)

Schedule

Date	Topic	Instructor	Scriber
08/09/2020, Tue	Lecture 01: History and Overview of Artificial Intelligence. [ slides ]	Y.Y.
10/09/2020, Thu	Lecture 02: Supervised Learning: Linear Regression with Python [ slides ] [ Reference ] To view .ipynb files below, you may try [ Jupyter NBViewer ] Linear Regression Python Notebook [ Regression.ipynb ]	Y.Y.
15/09/2020, Tue	Lecture 03: Linear Classification with Python [ slides ] [ Reference ] To view .ipynb files below, you may try [ Jupyter NBViewer ] Linear Classification Python Notebook [ Classification.ipynb ]	Y.Y.
17/09/2020, Thu	Lecture 04: Project 1 [ project1.pdf ] [ Reference ] Kaggle Contest: Predict Survival on the Titanic Kaggle Contest: Home Credit Default Risk Prediction	Y.Y.
22/09/2020, Tue	Lecture 05: Model Assessment and Selection: Subset, Forward, and Backward Selection [ 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) [ Selection.ipynb ]	Y.Y.
24/09/2020, Thu	Lecture 06: Model Selection: Ridge, Lasso, and Principal Component Regression [ 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) [ Selection.ipynb ]	Y.Y.
29/09/2020, Tue	Lecture 07: Decision Trees [ YY's slides ] [ Venue ] Rm 6602, Lift 31-32 [ Video ] [ HKUST Zoom ] [Reference]: To view .ipynb files below, you may try [ Jupyter NBViewer] Python Notebook for Decision Trees, Bagging, Random Forests and Boosting [ Tree.ipynb ]	Y.Y.
06/10/2020, Tue	Lecture 08: Bagging, Random Forests and Boosting [ YY's slides ] [ Venue ] Rm 6602, Lift 31-32 [Reference]: To view .ipynb files below, you may try [ Jupyter NBViewer] Python Notebook for Decision Trees, Bagging, Random Forests and Boosting [ Tree.ipynb ] [ Reports of Project 1 ] GitHub Repository for reports of Project 1 [ GitHub ] [ project1.pdf ] [ Groups ] 1. Ching Kwok Yan. Predict survival on the Titanic. [ poster (pptx) ] [ source (github) ] 2. HARJONO, Natasha Valerie; TSUI, Ying Tsz; WAHYU, Zoya Estella. Titanic: Machine Learning from Disaster. [ report (pdf) ] [ slides (pdf) ] 3. Liao Yi-han. Home Credit Default Risk. [ report (pdf) ] [ slides (pptx) ] 4. LIN Chi Wing. Logistic Regression Models on Titanic Survival Prediction. [ poster (pptx) ] [ source (github) ] 5. Ngai Nok Yiu, Cheung Hang Yee. Predict Survival on the Titanic. [ poster (pdf) ] [ source (github) ] [ slides (pptx) ] 6. WONG, Wing Kin. What sorts of people were more likely to survive? [ poster (pdf) ] [ source (github) ] [ slides (pdf) ] 7. LEE, Cheuk Yin. Machine Learning on predicting survival of Titanic’s passengers by using SVM model. [ report (pdf) ] [ slides (pptx) ]	Y.Y.
08/10/2020, Thu	Lecture 09: 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 ] [ Video ] [ HKUST Zoom ]	Y.Y.
13/10/2020, Tue	Today's class is cancelled due to Typoon level 8. [ Univeristy Notice ]	Y.Y.
15/10/2020, Thu	Lecture 10: Seminar [ Venue ] Zoom [ Speakers ] Liao Yi-han. Home Credit Default Risk. [ slides (pptx) ] HARJONO, Natasha Valerie; TSUI, Ying Tsz; WAHYU, Zoya Estella. Titanic: Machine Learning from Disaster. [ slides (pdf) ]	Y.Y.
20/10/2020, Tue	Lecture 11: Seminar [ Venue ] Zoom [ Speakers ] Ching Kwok Yan. LIN Chi Wing.	Y.Y.
22/10/2020, Thu	Lecture 12: Seminar. [ Title ] Applications of Quantitative Methods in Financial Market, Junxin Zhou (Jason), AQUMON, [ slides ] A brief intro to applications of Machine Learning in Finance, Kaijun Hou (Jeff), AQUMON, [ slides ] [ Abstract ] Jason will introduce some quantitative models in asset management applications based on his rich working experience. Jeff will introduce the business of AQUMON and cover some machine learning applications in risk management in other financial institutions. [ Bio ] Junxin Zhou (Jason): Senior Quantitative Researcher with passion and experience in quantitative research and investment strategy development. Working in the space of systematic alpha and asset allocation with proven track record, and keen on applying quantitative methods, data science techniques, and economic fundamentals to investment decision making. MSc degree in Financial Mathematics from HKUST and MSc degree in Information and Computational Science from Sun Yat-sen University. Chartered Financial Analyst (CFA), Financial Risk Manager (FRM) and SFC representative license holder of type 1, 4 & 9. Leading a quantitative research team with 5-6 quant analysts to develop multi-asset strategies and managing portfolios with total AUM of over 90 million Chinese Yuan. Kaijun Hou (Jeff): Graduated from HKUST in 2020 majored in Computer Science, Risk Management and Business Intelligence. He is the software developer at Magnum Research Limited. He is responsible for developing financial data center and electronic trading system in the company.	Y.Y.
27/10/2020, Tue	Lecture 13: Seminar [ Speakers ] Ngai Nok Yiu, Cheung Hang Yee. Titanic Survival Problem. [ slides (pptx) ] WONG, Wing Kin.	Y.Y.
29/10/2020, Thu	Lecture 14: Seminar and Final Project [ pdf ] [ Speakers ] LEE, Cheuk Yin.	Y.Y.
03/11/2020, Tue	Lecture 15: 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 ]	Y.Y.
05/11/2020, Thu	Lecture 16: Examples of Convolutional Neural Networks. [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 ]	Y.Y.
10/11/2020, Tue	Lecture 17: Topics in CNN: Visualization, Transfer Learning [ 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 ]	Y.Y.
12/11/2020, Thu	Lecture 18: 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 ]	Y.Y.
17/11/2020, Tue	Lecture 19: 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 ]	Y.Y.
19/11/2020, Thu	Lecture 20: 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 ]	A.W. Y.Y.
24/11/2020, Tue	Lecture 21: Seminar [ Speakers ] Lee Cheuk Yin. Discovery after reducing number of features on Titanic Problem. TSUI, Ying Tsz; HARJONO, Natasha Valerie. Titanic [ slides (pptx)]	Y.Y.
26/11/2020, Thu	Lecture 22: Seminar [ Speakers ] Ngai Nok Yiu and Cheung Hang Yee. Home Credit Default Risk. Liao Yi-Han. Semi-conductor Image Classification 2 mini.	Y.Y.
01/12/2020, Tue	Lecture 23: Seminar [ Speakers ] Ching Kwok Yan. Semi-conductor Image Classification. Lin Chi Wing. Semiconductor image classification via Histogram-based SVM.	Y.Y.
03/12/2020, Thu	Lecture 24: Seminar [ Speakers ] WAHYU, Zoya Estella Wong, Wing Kin [ Reports of Final Project ] GitHub Repository for reports of Project 2 [ GitHub ] [ project2.pdf ] 1. LEE, Cheuk Yin. Discovery after reducing number of features on Titanic Problem. [ report (pdf) ] [ slides (pptx) ] 2. TSUI, Ying Tsz; HARJONO, Natasha Valerie Titanic. [ report (pdf) ] [ source (github) ] [ slides (pptx)] 3. Ngai, Nok Yiu and Cheung, Hang Yee. Home Credit Default Risk. [ poster (pdf) ] [ source (github) ] [ slides (pptx) ] 4. Liao, Yi-han. Semi-conductor Image Classification 2 mini. [ report (pdf) ] [ source ] 5. Ching, Kwok Yan. Semi-conductor Image Classification. [ poster (pptx) ] 6. LIN, Chi Wing. Histogram-based SVM for Nexperia semiconductor image classification. [ report (ipynb) ] [ source (github) ] [ slides (pptx) ] 7. WAHYU, Zoya Estella. Nexperia Image Classification using CNN. [ report (pdf) ] [ slides (pdf) ] 8. WONG, Wing Kin. Nexperia Image Classification II with Deep Learning. [ poster (pdf) ] [ source (github) ] [ slides (pdf) ]	Y.Y.

by YAO, Yuan.