Artificial Intelligence and Data Science: The Application of Machine Learning in Data Analysis and Mining

Course 4: Artificial Intelligence and Data Science: The Application of Machine Learning in Data Analysis and Mining

I. Course Description

This course aims to inform students about how to use machine learning methods and tools for research. Students will learn how to ask research questions, design the study, collect the data, analyze the data, and interpret and present the results. And conduct research projects in small groups, which will be based on the methodology discussed in the course, with a final project presentation, draft research paper submission, and individual reflection.

The course covers regression modeling and machine learning. Students will have the opportunity to apply machine learning, statistical computing techniques to real-world datasets and organizational case studies throughout the course. This course is suitable for students who wish to gain a background in statistics or computer science, as well as for professionals who wish to learn how to apply statistical computing techniques to their work. Upon completion of the course, students will be able to analyze and interpret the organizational data and be able to make decisions based on the data.

II. Professor Introduction

Divakaran  Liginlal – Professor at Carnegie Mellon University

Professor Liginlal He is currently a professor of Information Systems Teaching at Carnegie Mellon University. Before joining Carnegie Mellon, he taught for nine years at the University of Wisconsin-Madison. During his time at the University of Wisconsin, he was awarded the Mabel Chippman Award for the Excellence in Teaching and the Lawrence J. Larson Award for Innovative Course Design. In addition, he won the Best Teacher award at Carnegie Mellon University in Qatar in 2013.

The professors research achievements in the fields of information security, human-computer interaction and decision support systems have been published in several famous academic journals including MIS journals, ACM Communications, IEEE-TKDE, IEEE-SMC and so on. The professors recent research papers include Predict Stock Market Return from Malicious Attack: A Comparative Analysis of Vector Self-Regression and Time-lapse Neural Network, Walking Case Analysis Writing: Cooperation between Information System and Writing Teaching and Research Section, Selection of Maximum Entropy in Knowledge-based identity authentication, etc.

III. Syllabus

1. Introduction to the data analysis
2. Sampling and data collection
3. Characteristic engineering and selection
4. Exploratory Data Analysis (EDA)
5. Regression analysis
6. Clustering: parts and levels
7. Classification: Decision tree
8. Classification: nearest neighbor, collection
9. Neural networks and deep learning
10. Machine learning in real-world scenarios