Illinois Institute of Technology, Chicago, USA
Course Descriptions
  • Data Analytics/Programming for Data Analytics: Data analytics is involved with multiple useful techniques which can be adopted to visualize and analyze data, make predictions, or extract knowledge/patterns to build intelligent applications or serve for business intelligence. This course focuses more on statistical data analytics. This course covers the topics in descriptive statistics (data, data types, descriptive measures and visualizations) and inferential statistics (hypothesis testing, ANOVA, predictive models). The course will introduce different statistical and analytical software, including R and Python, in order to perform statistical data analytics. In terms of the predictive models, we focus on multiple linear regressions, lasso and ridge regressions in this class. Classification will be given as advanced topics.
  • Data Mining and Machine Learning/Recommender Systems: Data mining is one of the useful tools to uncover patterns and relationships hidden in large data by using data analytics and knowledge discovery techniques. This course is a graduate level survey of concepts, principles and techniques related to data mining and machine learning. Students will be familiar with data preprocessing skills and the popular data mining and machine learning techniques, including the classical supervised learning (regressions and classification) and unsupervised learning(clustering and association rules analysis), as well as semi-supervised learning and ensemble learning. Students will also learn the related applictions, including text mining/NLP, Web mining, information retrieval and recommender systems.
  • Applied Artificial Intelligence: Artificial Intelligence (AI) is being used extensively to solve real-world complex problems. This course will deliver concepts and skills in both classical AI and modern AI. The classical AI refers to the fundamental knowledge in AI, such as search, logic, planning, uncertainty, game theory, markov models, etc. Modern AI, by contrast, will be concentrated on machine learning and deep learning techniques, especially their applications in NLP, object recognition, recommender systems, etc.
Teaching History
  • 2021 Fall
    ITMD 522: Data Mining & Machine Learning/Recommender Systems
    ITMD 524: Applied Artificial Intelligence & Deep Learning
    ITMD 597: Independent Studies (Research)
  • 2021 Spring
    ITMD 525: Topics in Data Science: Data Mining/Recommender Systems
    ITMT 595: Applied Artificial Intelligence
    ITMD 597: Independent Studies (Research)
  • 2020 Fall
    ITMD 525: Topics in Data Science: Data Mining/Recommender Systems
    ITMD 527: Data Analytics
    ITMD 597: Independent Studies (Research)
  • 2018 Fall to 2020 Spring
    ITMD 525: Topics in Data Science: Data Mining/Recommender Systems
    ITMD 527: Data Analytics
    ITMD 597: Independent Studies (Research)
  • 2019 Summer & 2020 Summer
    ITMD 527: Data Analytics
    ITMD 597: Independent Studies (Research)
  • 2018 Spring
    ITMD 525: Topics in Data Science: Data Mining
    ITMD 525: Topics in Data Science: Recommender Systems
    ITMD 527: Data Analytics
    ITMD 597: Independent Studies (Research)
  • 2017 Spring & Fall
    ITMD 523: Advanced Topics in Data Management
    ITMD 525: Topics in Data Science: Data Mining
    ITMD 527: Data Analytics
    ITMD 597: Independent Studies (Research)
  • 2016 Fall
    ITMD 510: Object-Oriented Programming
    ITMD 523: Advanced Topics in Data Management
    ITMD 525: Topics in Data Science: Data Mining
DePaul University, Chicago, USA
  • 2017 Spring, Adjunct Lecturer, IPD 346: Data Science for Business Program
  • 2017 Winter, Adjunct Lecturer, IPD 346: Data Science for Business Program
  • 2016 Fall, Adjunct Lecturer, IPD 346: Data Science for Business Program
  • 2016 Spring, Co-Lecturer, IPD 346: Data Science for Business Program
  • 2016 Winter, Guest Lecturer, CSC 480: Artificial Intelligence
  • 2015 Winter, Guest Lecturer, CSC 529: Advanced Data Mining
  • 2015 Winter, Guest Lecturer, IS 567: Fundamentals of Data Science