This course is an introduction to computational biology emphasizing the fundamentals of nucleic acid and protein sequence and structural analysis; it also includes an introduction to the analysis of complex biological systems. Topics covered in the course include principles and methods used for sequence alignment, motif finding, structural modeling, structure prediction and network modeling, as well as currently emerging research areas.

This resource explains the writing process steps and many prewriting strategies to help students come up with ideas for their college writing assignments. The resource was remixed from several other creative commons resources. It can be used as a textbook chapter for students to read and view the videos or as a prewriting assignment. It can also serve as an instructor resource to provide lecture notes and videos or in-class prewriting exercises.This resource was created to align to the English 1301 Student Learning Objective (SLO) "Develop ideas with appropriate support and attribution" as the initial idea-generation step of that process, and it also aligns to the English 1301 SLO "Demonstrate knowledge of individual writing processes," as it begins with explaining the writing process steps.  

Fundamentals of characterizing and recognizing patterns and features of interest in numerical data. Basic tools and theory for signal understanding problems with applications to user modeling, affect recognition, speech recognition and understanding, computer vision, physiological analysis, and more. Decision theory, statistical classification, maximum likelihood and Bayesian estimation, non-parametric methods, unsupervised learning and clustering. Additional topics on machine and human learning from active research.

The applications of pattern recognition techniques to problems of machine vision is the main focus for this course. Topics covered include, an overview of problems of machine vision and pattern classification, image formation and processing, feature extraction from images, biological object recognition, bayesian decision theory, and clustering.

Upon successful completion of this lesson, students will
- review the steps in the writing process (Prewriting, Drafting, Revising, Editing, Publishing).
- generate ideas about a topic using three prewriting strategies (Listing, Freewriting, Clustering).
- reflect and discuss the various prewriting strategies as well as their importance to the writing process.

A PowerPoint lesson is included.

Author: Brandi Morley
Editor: Mary Landry, C. Anneke Snyder
Supervisor: Terri Pantuso

Most algorithms in computer vision and image analysis can be understood in terms of two important components: a representation and a modeling/estimation algorithm. The representation defines what information is important about the objects and is used to describe them. The modeling techniques extract the information from images to instantiate the representation for the particular objects present in the scene. In this seminar, we will discuss popular representations (such as contours, level sets, deformation fields) and useful methods that allow us to extract and manipulate image information, including manifold fitting, markov random fields, expectation maximization, clustering and others. For each concept -- a new representation or an estimation algorithm -- a lecture on the mathematical foundations of the concept will be followed by a discussion of two or three relevant research papers in computer vision, medical and biological imaging, that use the concept in different ways. We will aim to understand the fundamental techniques and to recognize situations in which these techniques promise to improve the quality of the analysis.