Education is a cornerstone for nurturing curiosity, disseminating knowledge and equipping the forthcoming generation with the indispensable tools for innovation and leadership. My teaching approach fosters a stimulating and inclusive ambiance that encourages inquiry, discussions, and the exchange of diverse perspectives. Below are the details of the courses I am currently instructing, along with relevant course materials and contact information.
BMI 5780 – Programming for Biomedical Informatics (The Second Half: Machine Learning Sections)
In the latter half of this course, students are immersed in the essential machine-learning methodologies pertinent to Biomedical Informatics and big data analysis. The segment I instruct delves into various machine learning algorithms crucial for analyzing and deriving insights from complex biomedical data.
- Syllabus: Download Syllabus (PDF)
- Learning Objectives:
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- Acquire a solid understanding of machine learning algorithms, including linear regression, logistic regression, decision trees, random forests, gradient boosting, and neural networks.
- Apply these algorithms to real-world biomedical data, developing practical skills in data preprocessing, model training, evaluation, and interpretation.
- Foster the ability to compare machine learning algorithms and select the appropriate algorithm for a biomedical informatics problem.
- Teaching Methods: The instruction is delivered through lectures, hands-on lab exercises, and project-based assessments to ensure a thorough understanding and practical experience with the machine learning algorithms covered.
- Office Hours: By appointment
- Contact: qing.wu@osumc.edu |
Meta-Analysis in Health Science Research
This course elucidates the principles, methods, and applications of meta-analysis in health science research. Through a mixture of theoretical and practical examples, students are equipped with the skills to conduct and evaluate meta-analyses, enriching their research toolkit for evidence-based biomedical science and health research.
- Syllabus: Download Syllabus (PDF) — to be completed
- Learning Objectives:
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- Grasp the fundamental steps of conducting a meta-analysis, including study identification, data extraction, quality assessment, statistical analysis, and publication bias analysis.
- Gain hands-on experience with real-world examples and practical applications using Covidence, R, and STATA statistical software.
- Develop skills to conduct genome-wide meta-analysis and meta-learner of various machine-learning models.
- Teaching Methods: Engaging lectures, interactive discussions, group projects, and software tutorials are employed to foster a conducive learning environment for both theoretical comprehension and practical application.
- Office Hours: by appointment
Contact: qing.wu@osumc.edu