BINF 6210: Machine Learning for Bioinformatics

University of North Carolina, Charlotte

Introduction of commonly used machine learning methods in the field of bioinformatics. Topics include: dimension reduction using principal component analysis, singular value decomposition, and linear discriminant analysis, clustering using kmeans, hierarchical, expectation maximization approaches, classification using k-nearest neighbor and support vector machines. To help understand these methods, basic concepts from linear algebra, optimization, and information theory are explained. Application of these machine learning methods to solving bioinformatics problems are illustrated using examples from the literature.

Average GPA: 3.75

Grade distribution records: 80 students across 10 terms.

Grade distribution

Based on 80 student grade records across 10 terms and 2 professors.

Instructors

• Xiuxia Du 64 students, Average GPA 3.77
• Denis Jacob Machado 16 students, Average GPA 3.69