Experimental Bioinformatics Laboratory

Course Description

Provides fundamental concepts in bioinformatics with strong emphasis on hands-on training. Topics such as molecular biology, sequence alignment, biological databases, phytogeny,
and genomics including microarray and RNA-sequence analysis. Repeatable one time, but credit earned one time only.

Focus on bioinformatics approaches for functional genomics related to DNA, RNA, and protein. It will provide of virus, bacteria, and human genome and bioinformatics approaches to human disease.

This course introduces machine learning techniques with a strong emphasis on hands-on training. The course will cover topics on supervised learning algorithms (Nearest Neighbor, Naïve Bayes, Decision Trees, Classification Rule Learners, Regression Trees, Neural Networks, Support Vector Machines), unsupervised learning algorithms (association rules, k-means clustering), and meta-learning algorithms (bagging, boosting, and random forests). Students will use R and/or Python to explore and prepare data, train models, and evaluate and improve model performance. Moreover, the hands-on case studies with the essential theory will enable students to apply machine learning to their own projects.