Spring'21, Spring'22
Network science is a framework to analyze the complex systems of technological, biological, and cultural networks. This course will present the fundamentals of networks, mathematical toolsets to study and characterize networked data, and develop skills for network thinking. Special network topics such as network models, communities, and dynamics on networks will be presented.
Spring'23
Data science spans a large variety of disciplines and requires a collection of skills. This course is intended to tour the basic techniques of data science from manipulation and summarizing the important characteristics of a data set, basic statistical modeling, web programming and visualization.
Spring'22, Fall'23
This is an introductory machine learning course that will aim a solid understanding of the fundamental issues in machine learning (overfitting, bias/variance), together with several state-of-art approaches such as decision trees, linear regression, k-nearest neighbor, Bayesian classifiers, neural networks, logistic regression, and classifier combination. In addition to supervised approaches, unsupervised approaches will be covered, and model evaluations strategies will be introduced for different tasks.
