An introduction to the data science pipeline, i.e., the end-to-end process of going from unstructured, messy data to knowledge and actionable insights. Provides a broad overview of what data science means and systems and tools commonly used for data science, and illustrates the principles of data science through several case studies.

A broad introduction to machine learning and statistical pattern recognition. Topics include: Supervised learning: Bayes decision theory, discriminant functions, maximum likelihood estimation, nearest neighbor rule, linear discriminant analysis, support vector machines, neural networks, deep learning networks. Unsupervised learning: clustering, dimensionality reduction, PCA, auto-encoders. The course will also discuss recent applications of machine learning, such as computer vision, data mining, autonomous navigation, and speech recognition.

This course provides an introduction to the different types of data generated for bioinformatics analyses and data management principles required for scientific rigor and reproducibility. Students will explore different data sources, including sequencing data, 'omics data (e.g., proteomics, metabolomics, lipidomics), imaging data, and clinical data. The course will also cover data organization through topics such as management and curation of metadata, downloading data from and submitting data to public repositories, and using databases versus spreadsheets and tables.

This course provides an in-depth coverage of biological sequence alignment. Students will explore a variety of topics, including definitions, algorithms, and statistics for local, global, pairwise, and multiple alignments; scoring schemes; BLAST, BLAST variants, and similar programs; motif finding; and related topics.

This course provides an introduction to the fundamental data structures and algorithms underlying many parts of Bioinformatics. Students will explore standard data structures for efficient indexing and sequence search, including the suffix array and the FM-index, as will alignment-free methods for sequence comparison. This course will also introduce the fundamental algorithms in computational phylogenomics and biological network analysis. Finally, students will examine bioinformatics oriented applications of classic unsupervised learning algorithms (e.g., clustering and dimensionality reduction) and database techniques (e.g., sorting, selection, joining). The focus will be both on formal understanding of computational efficiency as well as the practical applications of these concepts.