Online courses directory (19947)

Please note: Our faculty members have indicated that several important changes need to be made in order to improve the course and your experience as a student. In 2013, we will be re-releasing this course under the simplified title: ARTH101: Art Appreciation and Techniques. Until then, you are welcome to work through this course at your own leisure; there

This is the capstone course in the Saylor Customer Service certificate program which is currently under development. CUST105 is designed to teach you about the customer service work environment, methods for successful customer relations, and career advancement tactics. Future courses in the certificate program will provide a solid knowledge base in the areas of computer skills, business communications, and best customer service practices.

This course provides students with a comprehensive introduction to computers. Students will explore a variety of topics in computing, such as the following: the components of a computer, common computer terminology, an introduction to the Internet, computer security and privacy, computer troubleshooting techniques, and steps to maintain the life of your computer. Through readings and videos, students will learn how to fully understand the basics of computer technology.

This course covers the development of programs containing a significant amount of knowledge about their application domain. The course includes a brief review of relevant AI techniques; case studies from a number of application domains, chosen to illustrate principles of system development; a discussion of technical issues encountered in building a system, including selection of knowledge representation, knowledge acquisition, etc.; and a discussion of current and future research. The course also provides hands-on experience in building an expert system (term project).

Analyzes computational needs of clinical medicine reviews systems and approaches that have been used to support those needs, and the relationship between clinical data and gene and protein measurements. Topics: the nature of clinical data; architecture and design of healthcare information systems; privacy and security issues; medical expertsystems; introduction to bioinformatics. Case studies and guest lectures describe contemporary systems and research projects. Term project using large clinical and genomic data sets integrates classroom topics.

This course presents the main concepts of decision analysis, artificial intelligence, and predictive model construction and evaluation in the specific context of medical applications. The advantages and disadvantages of using these methods in real-world systems are emphasized, while students gain hands-on experience with application specific methods. The technical focus of the course includes decision analysis, knowledge-based systems (qualitative and quantitative), learning systems (including logistic regression, classification trees, neural networks), and techniques to evaluate the performance of such systems.

Why has it been easier to develop a vaccine to eliminate polio than to control influenza or AIDS? Has there been natural selection for a 'language gene'? Why are there no animals with wheels? When does 'maximizing fitness' lead to evolutionary extinction? How are sex and parasites related? Why don't snakes eat grass? Why don't we have eyes in the back of our heads? How does modern genomics illustrate and challenge the field?

This course analyzes evolution from a computational, modeling, and engineering perspective. The course has extensive hands-on laboratory exercises in model-building and analyzing evolutionary data.

With the growing availability and lowering costs of genotyping and personal genome sequencing, the focus has shifted from the ability to obtain the sequence to the ability to make sense of the resulting information. This course is aimed at exploring the computational challenges associated with interpreting how sequence differences between individuals lead to phenotypic differences in gene expression, disease predisposition, or response to treatment.

PURPOSE OF COURSE…

Physics 140 offers introduction to mechanics, the physics of motion. Topics include: linear motion, vectors, projectiles, relative velocity and acceleration, Newton's laws, particle dynamics, work and energy, linear momentum, torque, angular momentum, gravitation, planetary motion, fluid statics and dynamics, simple harmonic motion, waves and sound. Course Level: Undergraduate This Work, Physics 140 - General Physics 1, by Gus Evrard is licensed under a Creative Commons Attribution license.

Organic Chemistry of Macromolecules covers the preparation, reactions, and properties of high molecular weight polymeric materials of both natural and synthetic origin. As a part of this course, U-M students collaboratively created and edited Wikipedia articles. Course Level: Graduate This Work, Chemistry 538 - Organic Chemistry of Macromolecules, by Anne McNeil is licensed under a Creative Commons Attribution-ShareAlike license.

This course examines both the

This course is broad, covering a wide range of topics that have to do with the post-pc era of computing. It is a hands-on project course that also includes some foundational subjects. Students will program iPAQ handheld computers, cell phones (series 60 phones), speech processing, vision, Cricket location systems, GPS, and more. Most of the programming will be using Python®, but Python® can be learned and mastered during the course.

This course was also taught as part of the Singapore-MIT Alliance (SMA) programme as course number SMA 5508 (Pervasive Computing).

Why has it been easier to develop a vaccine to eliminate polio than to control influenza or AIDS? Has there been natural selection for a 'language gene'? Why are there no animals with wheels? When does 'maximizing fitness' lead to evolutionary extinction? How are sex and parasites related? Why don't snakes eat grass? Why don't we have eyes in the back of our heads? How does modern genomics illustrate and challenge the field?

This course analyzes evolution from a computational, modeling, and engineering perspective. The course has extensive hands-on laboratory exercises in model-building and analyzing evolutionary data.

This course is a graduate level introduction to automatic discourse processing. The emphasis will be on methods and models that have applicability to natural language and speech processing.

The class will cover the following topics: discourse structure, models of coherence and cohesion, plan recognition algorithms, and text segmentation. We will study symbolic as well as machine learning methods for discourse analysis. We will also discuss the use of these methods in a variety of applications ranging from dialogue systems to automatic essay writing.

This subject qualifies as an Artificial Intelligence and Applications concentration subject.

This course introduces the theory of error-correcting codes to computer scientists. This theory, dating back to the works of Shannon and Hamming from the late 40's, overflows with theorems, techniques, and notions of interest to theoretical computer scientists. The course will focus on results of asymptotic and algorithmic significance. Principal topics include:

1. Construction and existence results for error-correcting codes.
2. Limitations on the combinatorial performance of error-correcting codes.
3. Decoding algorithms.
4. Applications in computer science.