Lab Introduction

Welcome to Machine Learning and Data Mining Lab at Ajou University

Data sets with millions of records and thousands of fields are increasingly common in business, engineering, medicine, and the sciences. With the amount of data doubling every few years the problem of uncovering hidden patterns or extracting useful information from such data sets is becoming an important practical issue.

Research on this topic focuses on key questions such as how can one build useful models which both allow us to make predictions and also aid us to figure out the underlying process of the data generation. Research projects in our lab use theories and techniques from the intersection of computer science, statistics, and mathematics, including foundational ideas from algorithms, artificial intelligence, multivariate data analysis, Bayesian estimation, and computational statistics (from statistics), and optimization and probability theory (from mathematics). Machine learning and pattern recognition, in particular, are central to our research, providing both a sound theoretical basis and a practical framework for developing useful data analysis algorithms.

Research activities in our lab range across areas as different as hospital fraud detection, direct marketing in CRM, oil price prediction, protein function prediction in bioinformatics, etc. We hope you find our web-site useful and encourage you to explore its contents (publications, courses, seminars, and other information).

Research Areas

Theory of Machine Learning or
Statistical Learning Algorithms

1. Semi-Supervised Learning
   • Graph-based SSL
   • Transductive Learning
2. Kernel Methods
   • Support Vector Machines(SVM)
   • Independent Component Analysis (ICA)
   • Kernel PCA, etc.
3. Connectionist Methods
   • Feed-Forward Neural Networks
   • Autoencoders
   • Self-Organizing Map (SOM), etc.
4. Graph-based Deep Learning
   • GNN(Graph Neural Network)
   • GCN(Graph Neural Networks), etc.

Applications of Machine Learning Methods in Various Fields

1. BioMedical Informatics
   • DNA/RNA/Protein Sequence Analysis
   • Protein Function Analysis
2. Financial Engineering
   • Stock and Futures Trading System
3. Customer Relationship Management
   • Customer Retention
   • Fraud Detection
   • Cross-sales
   • Direct Marketing

1. Data Analysis & Practice (Undergraduate)
   • Basics to Data Analysis: Descriptive/Inferential Statistics, Sampling, Data Screening,etc
   • Multivariate Analysis: Correlation/Regression/Factor/Discriminant Analysis
   • Time Series Analysis: AR(I)MA
   • Introduction to Up-to-date Data Mining Techniques
   • Practice with SAS or MINITAB
   • Term Project
2. Statistics Applications: (Undergraduate)
   • Understanding of the “descriptive statistics”
   • Understanding of the “discrete distributions and their applications”
   • Understanding of the “continuous distributions and their applications”
   • Foundation of “statistical inference” including parameter estimation and hypothesis testing
   • Understanding of “experimental design”
   • Foundation of “distribution-free statistics” (e.g., nonparametric hypothesis testing), etc.
   • With presentation of the corresponding theories, the course still maintains its practical approach with statistical S/W packages (e.g., MINITAB/EXCEL/SAS/MATLAB, etc)
3. Statistics Applications: (Undergraduate)
   • Understanding of the “descriptive statistics”
   • Understanding of the “discrete distributions and their applications”
   • Understanding of the “continuous distributions and their applications”
   • Foundation of “statistical inference” including parameter estimation and hypothesis testing
   • Understanding of “experimental design”
   • Foundation of “distribution-free statistics” (e.g., nonparametric hypothesis testing), etc.
   • With presentation of the corresponding theories, the course still maintains its practical approach with statistical S/W packages (e.g., MINITAB/EXCEL/SAS/MATLAB, etc)
4. Statistics Applications: (Undergraduate)
   • Understanding of the “descriptive statistics”
   • Understanding of the “discrete distributions and their applications”
   • Understanding of the “continuous distributions and their applications”
   • Foundation of “statistical inference” including parameter estimation and hypothesis testing
   • Understanding of “experimental design”
   • Foundation of “distribution-free statistics” (e.g., nonparametric hypothesis testing), etc.
   • With presentation of the corresponding theories, the course still maintains its practical approach with statistical S/W packages (e.g., MINITAB/EXCEL/SAS/MATLAB, etc)
5. Statistics Applications: (Undergraduate)
   • Understanding of the “descriptive statistics”
   • Understanding of the “discrete distributions and their applications”
   • Understanding of the “continuous distributions and their applications”
   • Foundation of “statistical inference” including parameter estimation and hypothesis testing
   • Understanding of “experimental design”
   • Foundation of “distribution-free statistics” (e.g., nonparametric hypothesis testing), etc.
   • With presentation of the corresponding theories, the course still maintains its practical approach with statistical S/W packages (e.g., MINITAB/EXCEL/SAS/MATLAB, etc)

Projects