This course is a faculty-directed student project or research involving laboratory work, computer modeling, or theoretical calculations that could be considered of an original nature. The level of study is appropriate for students in their first three years of study.

This course is a faculty-directed tutorial of appropriate topics that are not part of the formal curriculum. The level of study is appropriate for student in their first three years of study.

This course allows students to assist in a class or laboratory for which they have previously earned credit. The student will assist the instructor in the operation of the course. Assistance by the student may include fielding questions, helping in workshops, and assisting in review sessions. In the case of labs, students may also be asked to help with supervising safety practices, waste manifestation, and instrumentation.

This course addresses the fundamental concepts of bioinformatics, focusing on computational analysis of nucleic acids and proteins. Utilization of computational programs for analysis of individual and multiple sequences for functional and evolutionary information will be discussed. The computational laboratory will highlight the applications available for analysis of molecular sequences.

This course is a faculty-directed student project or research involving laboratory or field work, computer modeling, or theoretical calculations that could be considered of an original nature. The level of study is appropriate for students in their final two years of study.

This course is a faculty-directed tutorial of appropriate topics that are not part of the formal curriculum. The level of study is appropriate for student in their final two years of study.

Machine learning is a fast-developing field of artificial intelligence (AI) with many applications in life sciences. The huge amount of genomic data can be analyzed and interpreted by machine learning techniques. This course introduces basic concepts of machine learning models and demonstrates how these models can solve complex problems in life sciences. The course will start with a discussion of how machine learning is different than descriptive statistics, and introduce the machine learning toolkits through a tutorial. Main topics cover three branches of machine learning: supervised learning, unsupervised learning, and reinforcement learning. Instead of applying different machine learning methods to different datasets, the course aims to apply different methods to the same datasets so that students are able to compare the performance and pros/cons of the methods. Hands-on exercises will be provided in both lectures and weekly labs. A group project will be given at the end of the semester so that students can apply machine learning methods to the datasets they are interested in. By the end of this course, students will be able to identify the difference between a supervised (classification) and unsupervised (clustering) technique, identify which technique to apply for a particular dataset and need, engineer features to meet that need, and write code to carry out an analysis.

Machine learning is a fast-developing field of artificial intelligence (AI) with many applications in life sciences. The huge amount of genomic data can be analyzed and interpreted by machine learning techniques. This course introduces basic concepts of machine learning models and demonstrates how these models can solve complex problems in life sciences. The course will start with a discussion of how machine learning is different than descriptive statistics, and introduce the machine learning toolkits through a tutorial. Main topics cover three branches of machine learning: supervised learning, unsupervised learning, and reinforcement learning. Instead of applying different machine learning methods to different datasets, the course aims to apply different methods to the same datasets so that students are able to compare the performance and pros/cons of the methods. Hands-on exercises will be provided in both lectures and weekly labs. A group project will be given at the end of the semester so that students can apply machine learning methods to the datasets they are interested in. By the end of this course, students will be able to identify the difference between a supervised (classification) and unsupervised (clustering) technique, identify which technique to apply for a particular dataset and need, engineer features to meet that need, and write code to carry out an analysis.

Masters-level research by the candidate on an appropriate topic as arranged between the candidate and the research advisor.

Continuation of Thesis

This course is a faculty-directed, graduate level tutorial of appropriate topics that are not part of the formal curriculum.