Application of Machine Learning to Pharmaceutical Development Graduate Certificate
AvailableOn Campus & Online
ContactOffice of Graduate Admissions1.888.511.1306gradu[email protected]
Develop the skill set necessary to evaluate and apply the growing portfolio of algorithms from the open source ecosystem and deploy them in the appropriate context.
The application of machine learning continues to grow rapidly in a variety of industrial settings. In pharmaceutical development, machine learning methodologies have been adopted to accelerate process optimization and material characterization – a trend that is anticipated to rise with the increased rate of data digitization.
The Application of Machine Learning to Pharmaceutical Development Graduate Certificate program is designed to develop the skill set you need to evaluate and apply the growing portfolio of algorithms from the open source ecosystem and deploy them in the appropriate context. In addition to covering a multitude of machine learning algorithms such as generalized linear regression, non-linear regression, regularization methods, random-forest, neural networks, Markov processes, etc., the program emphasizes the generation of contextualized visualization tools to sufficiently present results to a wider, non-expert audience as well as health authorities – a key aspect of the successful implementation of machine learning in pharmaceutical development.
Who should consider this program?
The Application of Machine Learning to Pharmaceutical Development Graduate Certificate program is designed for students in the following programs who wish to pursue a career in pharmaceutical or related industries (chemical, food, etc.):
Graduates will be able to:
Identify data streams typically found in pharmaceutical organizations (but can be extended to other industries) that will benefit from the adoption of Machine Learning and the corresponding dissemination of results to improve and streamline the quality of decision making.
Organize teams to develop machine learning algorithms targeted to specific workflows aimed at increasing 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 state of the art machine learning models to address pharmaceutical development problems in the organization or improve on existing tools.
Graduates will be able to:
Understand and explain the underlying concepts powering relevant Machine Learning algorithms that will be covered throughout the program.
Understand and explain the applicability of the most frequently used loss functions for model fitting problems.
Identify appropriate Machine Learning algorithms to solve problems relevant to pharmaceutical development and rigorously evaluate the available data sets for applicability by applying tools in data visualization analysis and feature engineering.
Relate between sequential decision problems and decision trees, Bayesian networks and other Bayesian models arising in statistical learning and incorporate the model uncertainty in the underlying decision.
Apply reproducible research and version control to efficiently manage Machine Learning projects in a collaborative environment in situations and organizations typically found in industry.