East Lancashire Research 2008

East Lancashire Research 200

Enhancing Undergraduate AI Courses through Machine Learning Projects

It is generally recognized that an undergraduate introductory Artificial Intelligence course is challenging to teach. This is, in part, due to the diverse and seemingly disconnected core topics that are typically covered. The paper presents work funded by the National Science Foundation to address this problem and to enhance the student learning experience in the course. Our work involves the development of an adaptable framework for the presentation of core AI topics through a unifying theme of machine learning. A suite of hands-on semester-long projects are developed, each involving the design and implementation of a learning system that enhances a commonly-deployed application. The projects use machine learning as a unifying theme to tie together the core AI topics. In this paper, we will first provide an overview of our model and the projects being developed and will then present in some detail our experiences with one of the projects – Web User Profiling which we have used in our AI class

Unifying an Introduction to Artificial Intelligence Course through Machine Learning Laboratory Experiences

This paper presents work on a collaborative project funded by the National Science Foundation that incorporates machine learning as a unifying theme to teach fundamental concepts typically covered in the introductory Artificial Intelligence courses. The project involves the development of an adaptable framework for the presentation of core AI topics. This is accomplished through the development, implementation, and testing of a suite of adaptable, hands-on laboratory projects that can be closely integrated into the AI course. Through the design and implementation of learning systems that enhance commonly-deployed applications, our model acknowledges that intelligent systems are best taught through their application to challenging problems. The goals of the project are to (1) enhance the student learning experience in the AI course, (2) increase student interest and motivation to learn AI by providing a framework for the presentation of the major AI topics that emphasizes the strong connection between AI and computer science and engineering, and (3) highlight the bridge that machine learning provides between AI technology and modern software engineering

Silver Anniversary in Smithfield

Publication celebrating Bryant\u27s 25 years in Smithfield, Rhode Island and the inauguration of Bryant\u27s 7th president, Ronald K. Machtley. The theme of the inauguration was A Silver Anniversary ... A Golden Opportunity

WSU Research News, Winter/Spring 2011

A twenty-four page newsletter of the WSU Research News. The WSU Research News was published monthly beginning in June of 1968 and issued by the Office of Research Development. This newsletter was created to provide information to the WSU faculty about the availability of outside funds for research and educational programs, new developments that may affect availability of funds, and general information on research and educational activities at Wright State University.https://corescholar.libraries.wright.edu/wsu_research_news/1202/thumbnail.jp

The Rock, Summer 2016 (vol. 85, no. 2)

https://poetcommons.whittier.edu/rock/1006/thumbnail.jp

'Democracy begins in conversation’: the phenomenology of problem-based learning and legal education

Learning is complex for any number of reasons. One of these is that it doesn’t take place in a laboratory: it happens in real places, within and between real people, and as a consequence it takes place in multi-factorial environments. At every stage of learning in Higher Education (HE), from student choice of institution and programme, to the transfer of learning from theory to practice, to a single institution’s or a teacher’s evaluation of teaching and learning, there are many causal factors that affect educational process and outcome. The complexities and variables created by the interaction of such multiple factors, well known in the field of education, make learning a highly complex phenomenon to analyse and understand

Can We Bridge the Gap? Mathematics and the Life Sciences, Part 1–Calculus-Based Modules, Programs, Curricula

This editorial serves as an introduction to Part 1 of the Special Issue Mathematics and the Life Sciences–a collection of articles showcasing ideas, examples, pedagogical frameworks, and curricular materials aiming to bridge the stubbornly persistent gap at the undergraduate level between the mathematical and the life sciences. The special issue features authors from public and private institutions of diverse types, sizes, and geographic locations: community colleges, liberal arts colleges, and research-oriented universities. We hope this special issue will serve as a resource to faculty who seek to make changes to their own course(s) or initiate curriculum reforms at their own schools. Part 1 focuses on educational initiatives that are appropriate for Calculus classes or require calculus as a prerequisite. Part 2 of the special issue features course materials and programs based on discrete mathematics, computational approaches, and statistics. Part 2 also includes articles on internship programs and co-curricular opportunities