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 tillJune 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
08:00-11:30Module 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
13:00-16:30Module 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, logisticregressionand 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