Teaching

Courses

  • Course Description: This course provides an overview of what big data is and explores its characteristics. It introduces the fundamental technologies, platforms, and methods that enable Big Data analysis, and covers how to acquire, store, and analyze very large amounts of information to complete Big Data analysis tasks. Students will gain hands-on experience in real-world applications of Big Data such as in cyber-physical systems and health informatics. Most of the work in this course will be team-based and task-oriented.
  • Course Objectives: Students will be able to acquire, store, access, visualize, and analyze big data. Get familiar with the structure of Hadoop then learn the analytic aspect of big data as the main focus of the course such as clustering and classification, decision trees and random forests, and dimensionality reduction. (PDF)
  • Course Description: This course provides an in-depth study of advanced data mining, data analysis, and pattern recognition concepts and algorithms. Course content builds upon a first data mining course and explores advanced machine learning algorithms, high-dimensional data and temporal data, and advanced methods and applications to deal with dynamic stream data. Various applications will be considered, including social networks and health informatics.
  • Course Objectives: Students will be able to understand the research methods applied in the field of data mining and knowledge discovery and conduct an end-to-end data mining project and document and present the results. They will be able to use the techniques covered in the course for building an end-to-end data mining project. They will get familiar with methods for modeling, analysis, and evaluating the development of data dependent modeling systems. (PDF)
  • Course Description: This course provides an advanced/Theoretical aspect of artificial intelligence (AI) and explores different categories of AI models. This course cover what do Google search, image/video recognition, machine translation, speech recognition, autonomous driving, and many other automated learning and adaptive systems have in common. This course illustrate how AI methods model and tackle these complex real-world problems with rigorous mathematical tools.
  • Course Objectives: Students will learn the foundational principles that makes intelligent systems provide solutions to these applications and practice implementing some of those systems. Specific topics include machine learning, search and optimization, Markov decision processes, constraint satisfaction, and graphic models. The main goal of the course is to prepare students with the tools to undertake new AI solution to the real-world applications that they might encounter in life.
  • Course Description: This course provides an introduction to Decision Support and expert systems. It discusses basic topics of knowledge representation, logic, reasoning, and learning under uncertainty. The course introduces students to intelligent methods in order to build an integrated decision support system. Topics covered include: methods for modeling, analysis, and evaluating the development of data dependent decision support systems. The course also covers user interface design for intelligent learning and feedback systems. Finally, real case studies for proactive health-care decision support systems involving continuous behavioral and biological data monitoring and feedback will be introduced and effective inference systems and knowledge extraction in short term and long term manner will be investigated.
  • Course Objectives: Students will be able to use the techniques covered in the course for building end-to-end knowledge discovery systems. They will get familiar with methods for modeling and analysis such as rule-based, fuzzy, and neural network system. They will be able to evaluate the development of data dependent decision support systems. (PDF)