Courses Taught by William Wen
BIOSTAT626: Machine Learning For Health Sciences
- Graduate level
- Residential
- Winter term(s) for residential students;
- 3 credit hour(s) for residential students;
- Instructor(s): William Wen (Residential);
- Prerequisites: BIOSTAT 601
- Description: This is a 3-credit course introducing modern machine learning algorithms and data analytics for prediction, classification and data pattern recognition, with an emphasis on their applications in health data sciences.
- Learning Objectives: (a) To understand the foundation and rules to use machine learning techniques for handling data from the health sciences (b) To develop practical knowledge and understanding of modern machine learning techniques for health big data analysis. (c) To learn and master basic software and programming skills to apply machine learning algorithms in analyzing data arising from the health sciences.
- Syllabus for BIOSTAT626
| Department | Program | Degree | Competency | Specific course(s) that allow assessment | BIOSTAT | Health Data Science | MS | Apply basic informatics and computational techniques in the analysis of big health data, and interpret results of statistical analysis | BIOSTAT626 | BIOSTAT | Health Data Science | MS | Master the theoretical foundations to design and apply machine learning algorithms in biomedical applications. Understand the process of developing and assessing machine learning algorithms, including design principles, parameter estimations, and performance evaluation. Understand a diverse set of commonly used machine learning algorithms in both supervised and unsupervised learning scenarios | BIOSTAT626 |
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BIOSTAT680: Applications of Stochastic Processes I
- Graduate level
- Residential
- Fall term(s) for residential students;
- 3 credit hour(s) for residential students;
- Instructor(s): William Wen (Residential);
- Prerequisites: Biostat 601 and Math 450 or equiv
- Description: Conditional distributions, probability generating functions, convolutions, discrete and continuous parameter, Markov chains, medical and health related applications.
- Syllabus for BIOSTAT680
| Department | Program | Degree | Competency | Specific course(s) that allow assessment | BIOSTAT | PhD | Master the theoretical foundations of probability theory and apply the theoretical principles in probabilistic modeling. | BIOSTAT680 |
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