PKU

MATH 4995: Capstone Project for Data Science
Fall 2021

Course Information

Synopsis

This course is about projects with real world data for students in data science.

Prerequisite: (statistical) machine learning.

Instructors:

Yuan Yao

Time and Place:

TuTh 3:00-4:20pm, Rm 5510, Lift 25-26, Zoom online, HKUST
Tutorial session: Tu 6:00-6:50pm, Rm 5510, Lift 25-26, 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

Kaggle Contest: Predict Survival on the Titanic .

Kaggle Contest: Home Credit Default Risk Prediction .

Kaggle Contest: Nexperia Image Classification (Second Stage, on-going) .

Kaggle Contest: Nexperia Image Classification (First Stage, finished) .

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

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. LIANG, Zhicong < zliangak (add "AT connect DOT ust DOT hk" afterwards) >

Schedule

Date Topic Instructor Scriber
02/09/2021, Tue Lecture 01: History and Overview of Artificial Intelligence. [ slides ] Y.Y.
07/09/2021, Tue Lecture 02: Supervised Learning: Linear Regression with Python [ slides ] Y.Y.
09/09/2021, Thu Lecture 03: Linear Classification with Python [ slides ] Y.Y.
14/09/2021, Tue Lecture 04: Project 1 [ project1.pdf ], Model Assessment and Selection I: Subset, Forward, and Backward Selection [ slides ] Y.Y.
16/09/2021, Thu Lecture 05: Model Assessment and Selection II: Ridge, Lasso, and Principal Component Regression [ slides ]
Y.Y.
21/09/2021, Tue Lecture 06: Decision Trees [ slides ]
Y.Y.
23/09/2021, Thu Lecture 07: Bagging, Random Forests and Boosting [ slides ]
Y.Y.
28/09/2021, Tue Lecture 08: Support Vector Machines I [ slides ]
Y.Y.
30/09/2021, Thu Lecture 09: Support Vector Machines II [ slides ]
Y.Y.
05/10/2021, Tue Lecture 10: An Introduction to Convolutional Neural Networks [ slides ]
Y.Y.
07/10/2021, Thu Lecture 11: Examples of Convolutional Neural Networks.
Y.Y.
12/10/2021, Tue Lecture 12: Seminar
Y.Y.
19/10/2021, Tue Lecture 13: Seminar
Y.Y.
21/10/2021, Thu Lecture 14: Seminar and Project 2 [ (pdf) ]
Y.Y.
26/10/2021, Tue Lecture 15: An Introduction to Recurrent Neural Networks (RNN) [ slides ]
Y.Y.
28/10/2021, Thu Lecture 16: Long-Short-Term-Memory (LSTM) [ slides ]
Y.Y.
02/11/2021, Tue Lecture 17: Attention and Transformer [ slides ]
Y.Y.
04/11/2021, Thu Lecture 18: BERT (Bidirectional Encoder Representations from Transformers) [ slides ]
Y.Y.
09/11/2021, Tue Lecture 19: An Introduction to Reinforcement Learning and Deep Q-Learning [ 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 ]
Y.Y.
11/11/2021, Thu Lecture 20: An Introduction to Reinforcement Learning: Policy Gradient and Actor-Critic Methods [ 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 FinRL example 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 ]
  • Prof. Michael Kearns, University of Pennsyvania, Algorithmic Trading and Machine Learning, Simons Institute at Berkeley [ link ]
Y.Y.
16/11/2021, Tue Lecture 21: An Introduction to Unsupervised Learning: PCA, AutoEncoder, VAE, and GANs [ slides ]
Y.Y.
18/11/2021, Thu Lecture 22: Seminar
Y.Y.
23/11/2021, Tue Lecture 23: Seminar
Y.Y.
25/11/2021, Thu Lecture 24: Seminar
Y.Y.
30/11/2021, Tue Lecture 25: Seminar
Y.Y.
by YAO, Yuan.