Graduate

Summary of Course Content

None

Illustrative Reading

None

Potential Course Overlap

None

Summary of Course Content

None

Illustrative Reading

None

Potential Course Overlap

None

Summary of Course Content

This course will address the issues involved in software development for reuse. The course will expose
students to technical topics relating to software reuse: design patterns, component-based development,
domain-specific languages, and code generation techniques such as: syntactic and scoping hygene,
partial evaluation, de-forestation. Concepts will be re-enforced via the case-study of specific languages
and systems.

Students will undertake 2 assignments and a project. The project, which will carry the bulk of the credit,
will involve implementing a domain-specific platform which enables the rapid development of a class of
applications for a specific domain.

Design Statement

ECS 260 students will undertake a significant project, counting for 50% of the credit of the course, where
they will design a domain-specific language (or extensions thereof) and an implementation of the
language by writing a compiler. This project will require the students to exercise design judgement,
balancing such goals as ease-of-use, evolvability, and performance. Examples from the past include: a
policy language for security policies, and compiler that generates wrappers to enforce such policies; a
language to specify curriculum requirements and a compiler that generates a checker to validate an
individual student's academic plans.

ABET Category Content

Engineering Science: 1 unit
Engineering Design: 2 units

Illustrative Reading

A collection of papers and notes that will be distributed by the instructor.

Potential Course Overlap
The course does not have a significant overlap with any other course. It does however, complement the
material in 240, since we describe the use of high-level programming languages to improve software
productivity in narrow domains. The primary focus of the course is on techniques to achieve higher
levels of software reuse in narrow application domains: techniques include design patterns, distributed
object systems, software architectures, and domain-specific and other transformational techniques.

Summary of Course Content

1. Overview; claims; ways to evaluate learning and discovery systems
2. Inductive learning (learning conjunctive concepts from examples)
3. Learning decision trees
4. Conceptual clustering
5. Learning and discovery of heuristics for problem-solving
6. Discovery of numerical laws
7. Learning from analogies, learning from experiments
8. Deductive learning (explanation-based learning and the connection to program optimization)

Illustrative Reading
T.M. Mitchell, Machine Learning, McGrawHill, 1997

Potential Course Overlap
This course does not have a significant overlap with any other course. It covers some of the topics as in EEC 207, but does so at a more software-related level. Applications in computational science are emphasized throughout.

Summary of Course Content:

1. Data and Image Models
2. Visual Perception and Cognition
3. Process of Data Visualization
4. Space Perception and Display of Data in Space
5. Purpose of Color and Principles of Using Color in Visualization and UI Design
6. Interaction Models
7. Browsing and Navigation
8. Multivariate and Multidimensional Data
9. Trees and Graphs for Representing Hierarchies and Complex Relations
10. Evaluation of Visualization Design

Illustrative Reading

• E Tufte, Envioning Information, Graphics Press, 1990
• C Ware, Information Visualization, 2nd Edition, Morgan Kaufmann, 2004

Potential Course Overlap
Comparison of this course with other existing courses does not indicate any significant overlap. ECS 163 places significant emphasis on user interface design and provides adequate background for ECS 272. ECS 272 focuses on advanced research topics and prepares students for pursuing research in information visualization. ECS 273 was designed for non-CS major graduate students with the objective of exposing them to the latest visualization technology and how to apply the technology to particular application domains of their interest.

Summary of Course Content

Visual analytics is the science of analytical reasoning facilitated by interactive visual interfaces. Students will learn and practice how to design, realize, and evaluate visual analytics methods integrating interactive visualization, statistical analysis methods, machine learning, and high-performance computing techniques for solving complex data analysis problems found in real-world applications.

Lectures will cover the following topics:

1. Introduction to visualization
2. Visual analytics pipelines
3. Data cleaning and uncertainty
4. Interaction techniques
5. High dimensional data
6. Network data
7. Event sequence data
8. Time series data
9. Text data
10. Geospatial data
11. Collaborative analysis
12. Visualization and machine learning
13. Evaluation  

Illustrative Reading
Illuminating the Path: The Research and Development Agenda for Visual Analytics. James J. Thomas and Kristin A. Cook. United States Department of Homeland Security, 2005

Potential Course Overlap
A few subtopics on visualization fundamentals overlap with parts of ECS272.