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Teaching
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371-2-1721 Statistical Data Mining for Research Students (graduate course)
Data mining is the art of extracting useful patterns from large bodies of data; finding seams of actionable knowledge in the raw ore of information. The rapid growth of computerized data, and the computer power available to analyze it, creates great opportunities for data mining in engineering, medicine, science, government, etc. The aim of this course is to give the research students the practical tools and the theoretical knowledge to help him take advantage of these opportunities in a productive and informed way. Data mining is related to statistics and to machine learning, but has its own aims and scope. Statistics is a mathematical science, studying how reliable inferences can be drawn from imperfect data. Machine learning is a branch of engineering, developing a technology of automated induction.
When? the course is given in winter semester of every year
371-2-1731 Advanced Topics in Data Mining (graduate course)
This graduate-level class is a sequel of Statistical Data Mining for Research Students. It will cover several advanced machine learning topics, including graphical models, kernel methods, boosting, bagging, semi-supervised and active learning. The focus of the class will be graphical models and kernel methods, which are currently the major paradigms for building advanced and sophisticated machine learning models for complex real world problems.
When? the course is given in spring semester of every year
[Syllabus] [website]
371-1-0271 Introduction to statistics and data processing (undergraduate course)
This is an introductory course in statistical theory and methodology, aimed at the goals of understanding the fundamental principles of statistical reasoning, achieving proficiency in data analysis, and developing written communications skills. To these ends, topics include descriptive statistics and data analysis, fundamental concepts of the theory of estimation and hypothesis testing, and methodology such as sampling, analysis of variance, and least squares estimation. The laboratory includes computer-based simulation using OMNet++ with application to network performance analytics.
When? the course is given in spring semester of every year
[Syllabus] [website]
371-1-1602 Principles of Computer Science for Communication (undergraduate course)
This first course in computer science develops foundational skills in computer programming using the Python programming language. The course is suitable for engineering majors, and others interested in a rigorous introduction. The course will introduce the process of developing algorithms to solve problems, and the corresponding process of developing computer programs to express those algorithms.
When? the course is given in winter semester of every year
371-1-0341 Data Structures (undergraduate course)
The purpose of this course is to provide the students with solid foundations in the basic concepts of programming and algorithms design. The main objective of the course is to teach the students how to select and design data structures and algorithms that are appropriate for problems that they might encounter. This course is also about showing the correctness of algorithms and studying their computational complexity. This course offers the students a mixture of theoretical knowledge and practical experience.
When? the course is given in winter semester of every year
[Syllabus] [website]
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