Size and time scales. Historical astronomy. Astronomical instrumentation. Stars: spectra and classification. Stellar structure equations and survey of stellar evolution. Stellar oscillations. Degenerate and collapsed stars; radio pulsars. Interacting binary systems; accretion disks, x-ray sources. Gravitational lenses; dark matter. Interstellar medium: HII regions, supernova remnants, molecular clouds, dust; radiative transfer; Jeans' mass; star formation. High-energy astrophysics: Compton scattering, bremsstrahlung, synchrotron radiation, cosmic rays. Galactic stellar distributions and populations; Oort constants; Oort limit; and globular clusters.

This is a communication intensive supplement to Linear Algebra (18.06). The main emphasis is on the methods of creating rigorous and elegant proofs and presenting them clearly in writing.

How people make sense of their worlds symbolically through myth, ritual, metaphor, and cosmology. The structure of symbols, the natural and social elements they draw on, their social use, and the messages they convey. Students learn to record and analyze myth and ritual.

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.

Introductory Writing Course developed through the Ohio Department of Higher Education OER Innovation Grant. The course is part of the Ohio Transfer Module and is also named TME002. This work was completed and the course was posted in September 2018. For more information about credit transfer between Ohio colleges and universities, please visit: www.ohiohighered.org/transfer.Team LeadRachel Brooks-Pannell                       Columbus State Community CollegeContent ContributorsCatherine Braun                                  Ohio State UniversityMartin Brick                                         Ohio Dominican UniversityPeter Landino                                      Terra State Community CollegeBrian Leingang                                    Edison State Community CollegeBonnie Proudfoot                                Hocking CollegeJason Reynolds                                  Southern State Community CollegeMarie Stokes                                       Stark State CollegeLibrarianKatie Foran-Mulcahy                           University of Cincinnati Clermont CollegeReview TeamAnna Bogen                                        Marion Technical CollegeSteven Mohr                                       Terra State Community CollegeKelsey Squire                                      Ohio Dominican University

IntroductionThis portion of the course is simply to provide explanation, examples, and samples of Genres or Rhetorical Modes of writing students might be assigned in First- and Second-Year Writing courses. This module assumes that instructors will utilize other learning objectives (e.g. Writing as a Process, Collaboration, Grammar and Style, Critical Thinking, Conducting Research, and Understanding Rhetorical Situations, etc.) to teach writing, using this section merely as illustrations of academic genres or rhetorical modes.  

How to Use This GuideThis document is intended to highlight resources that can be used to address the topic of Genres that might be assigned in a First- and/or Second-Year Writing Course. All resources are Open Access and can be downloaded to a Course Management System via hyperlink.IntroductionThis portion of the course is simply to provide explanation, examples, and samples of Genres or Rhetorical Modes of writing students might be assigned in First- and Second-Year Writing courses. This module assumes that instructors will utilize other learning objectives (e.g. Writing as a Process, Collaboration, Grammar and Style, Critical Thinking, Conducting Research, and Understanding Rhetorical Situations, etc.) to teach writing, using this section merely as illustrations of academic genres or rhetorical modes.  

This course focuses on the problem of supervised learning from the perspective of modern statistical learning theory starting with the theory of multivariate function approximation from sparse data. It develops basic tools such as Regularization including Support Vector Machines for regression and classification. It derives generalization bounds using both stability and VC theory. It also discusses topics such as boosting and feature selection and examines applications in several areas: Computer Vision, Computer Graphics, Text Classification and Bioinformatics. The final projects and hands-on applications and exercises are planned, paralleling the rapidly increasing practical uses of the techniques described in the subject.

The main aims of this seminar will be to go over the classification of surfaces (Enriques-Castelnuovo for characteristic zero, Bombieri-Mumford for characteristic p), while working out plenty of examples, and treating their geometry and arithmetic as far as possible.