Application of Machine Learning to Pharmaceutical Development Graduate Certificate

Program Overview

The Application of Machine Learning to Pharmaceutical Development Graduate Certificate program equips students with the skills to evaluate and apply machine learning algorithms in the pharmaceutical industry. By understanding machine learning concepts and their applicability in pharmaceutical development, graduates can identify data streams suitable for machine learning, develop algorithms, and effectively present results. The program prepares students for careers in pharmaceutical or related industries, focusing on the use of contextualized visualization tools for successful machine learning implementation.

Program Outline

Degree Overview:

The Application of Machine Learning to Pharmaceutical Development Graduate Certificate program is designed to equip students with the skills necessary to evaluate and apply machine learning algorithms in pharmaceutical development. The program focuses on developing a comprehensive understanding of machine learning algorithms, their applications in pharmaceutical development, and the ability to present results effectively to both technical and non-technical audiences.

Objectives:

Graduates of this program will be able to:

• Identify data streams within pharmaceutical organizations (and potentially other industries) that can benefit from machine learning and effectively disseminate results to improve decision-making.
• Organize teams to develop machine learning algorithms tailored to specific workflows, aiming to increase access and adoption of sophisticated analysis.
• Diagnose existing Data Science workflows for potential improvements in reliability, efficiency, and speed.
• Explore and evaluate the adoption of cutting-edge machine learning models to address pharmaceutical development challenges within organizations or enhance existing tools.

Educational Outcomes:

Graduates will demonstrate the ability to:

• Understand and explain the underlying concepts of relevant machine learning algorithms covered in the program.
• Relate sequential decision problems to decision trees, Bayesian networks, and other Bayesian models in statistical learning, incorporating model uncertainty into decision-making.
• Apply reproducible research and version control to efficiently manage machine learning projects in a collaborative environment, typical of industry settings.

Teaching:

• The program is taught by faculty actively involved in research and publishing groundbreaking work.
• The program utilizes the best tools for research.

Careers:

The program prepares students for careers in pharmaceutical or related industries (chemical, food, etc.).

Other:

• The program emphasizes the generation of contextualized visualization tools to effectively present results to a wider audience, including non-experts and health authorities.
• This is a key aspect of successful machine learning implementation in pharmaceutical development.
• The program covers a wide range of machine learning algorithms, including generalized linear regression, non-linear regression, regularization methods, random-forest, neural networks, and Markov processes.
• The program is available both on campus and online.