Course outline
Data Science is an emerging and inherently interdisciplinary field, with Statistical Machine Learning techniques forming a key set of skills. This course will cover a wide range of popular supervised and unsupervised learning methods including regression, classification, deep learning, model selection and Bayesian computation. The course will also offer an insight into how these statistical methodologies are applied into business applications such as credit scoring and customer behavior analysis.
Lecturer: Associate Professor Minh-Ngoc Tran, University of Sydney Business School
Minh-Ngoc is with the Discipline of Business Analytics, University of Sydney Business School. He obtained a BSc and a MSc both in Mathematics from the Vietnam National University at Hanoi, and a PhD in Statistics in 2012 from the National University of Singapore. Minh-Ngoc’s main research interest is Bayesian computation and statistical Machine Learning with a special focus on Variational Bayes. He is also working on bringing state-of-the-art quantum computation techniques into data analysis. He is interested in promoting the use of modern Bayesian computation techniques in Cognitive Science, Consumer Behaviour and Financial Econometrics. Minh-Ngoc’s research has been published in many top-tier statistical journals and conferences. His research has been well funded with more than $4 million including four ARC grants. He is also an enthusiastic educator.
Prerequisites
This course is suitable for students who are interested in Data Science and have some background in statistics or related fields such as computer science, mathematics and econometrics. Also, some basic knowledge of programming is useful, as the practical part of the course will be delivered using the Python statistical software.
Register the course
Please scan this QR code to register. Registration is open till June 05, 24:00.
本课程受中央财经大学引智项目支持。
Schedule and contents
Day 1 |
June 8 Wednesday |
Course: Statistical Machine Learning |
08:00-11:30 Module 1: Introduction to Statistical Learning |
1 |
Overview of the course |
2 |
Introduction to Statistical Learning |
3 |
Linear Regression 1: Simple Linear Regression |
Lab 1 |
Introduction to Python |
13:00-16:30 Module 2: Linear Regression |
4 |
Linear Regression 2: Multiple Linear Regression |
5 |
Linear Regression 3: Multiple Linear Regression |
6 |
Linear Regression 4: Lasso, ridge and other regularizations |
Lab 2 |
Introduction to Python |
Day 2 |
June 9 Thursday |
Course: Statistical Machine Learning |
08:00-11:30 Module 1: Classification |
1 |
Classification 1: Introduction |
2 |
Classification 2: kNN, logistic regression and advanced concepts |
3 |
Classification 3: kNN, logistic regression and advanced concepts |
Lab 1 |
Python for data analysis |
13:00-16:30 Module 2 : Model selection |
4 |
Model selection 1: Introduction |
5 |
Model selection 2: Bias-variance decomposition |
6 |
Model selection 3: Popular model selection methods |
Lab 2 |
Python for data analysis |
Day 3 |
June 15 Wednesday |
Course: Statistical Machine Learning |
08:00-11:30 Module 1: Deep learning |
1 |
Deep Learning 1: Introduction |
2 |
Deep Learning 2: Feedforward neural networks |
3 |
Deep Learning 3: Feedforward neural networks |
Lab 1 |
Linear regression with Python |
13:00-16:30 Module 2 : Deep learning |
4 |
Deep Learning 4: Backpropagation algorithm |
5 |
Deep Learning 5: Recurrent neural networks |
6 |
Deep Learning 6: Recurrent neural networks |
Lab 2 |
Linear regression with Python |
Day 4 |
June 16 Thursday |
Course: Statistical Machine Learning |
08:00-11:30 Module 1: Bayesian computation |
1 |
Introduction to Bayesian statistics |
2 |
Introduction to Bayesian statistics |
3 |
Simple Monte Carlo methods |
Lab 1 |
Deep learning with Python |
13:00-16:30 Module 2 : Bayesian computation |
4 |
Simple Monte Carlo methods |
5 |
MCMC and Variational Bayes |
6 |
MCMC and Variational Bayes |
Lab 2 |
Deep learning with Python |
