Jerusalem2020J2IL

user stats

Member SinceNovember 14, 2013
Last VisitAugust 7, 2016

Contact

Jerusalem2020J2IL

personal statement

Introduction
Penn Foster High School/College student taking up High School Information Technology Concentration program taking five electives subjects of Computer Applications-Advanced PC Applications-Introduction to Programming -HTML Coding-Introduction to Database Technology.My information technology electives are transferable to the Penn Foster College Computer Information Systems Associate Degree Program or Penn Foster Technology career diploma programs. While I'm studying for these courses at my own pace Also I will be brushing up with CodePlex projects from Digital media arts instructional design-Mathematics computation-Information Management Systems and Software engineering. As a Software engineering CodePlex developer and editor with a background in object-oriented programming using C# C++ or Java (at the level of data structures) and discrete mathematics this concentration provides training in the use of systemactic engineering principles applied to the design-construction and maintenance of large software applications. Topic: Computer Science Programming Microsoft .NET
Course Number Integer:
33
Programming Microsoft .NET

The first half of this course covers the .NET framework in some detail. Covered topics include the type system, exceptions, garbage collection, threading, and reflection. The second half surveys the additional functionality built on top of the .NET framework. Topics include WCF, WPF, Azure, Windows Phone, and others to be selected based on class interest and availability of guest speakers. Extensive programming homework required. Formerly CSCI E-237.

Prerequisites: familiarity with either the C# or VB.NET languages; at least one year of industrial experience in object-oriented programming.

Labs: Optional sections to be arranged.
Subject:
Computer Science
CSCI E-33
Course Code Display:
CSCI E-33
Computer Science
Course Credits: 4
33
Data Structures
Course Number Integer:
22
Data Structures

This course is a survey of fundamental data structures for information processing, including lists, stacks, queues, trees, and graphs. It explores the implementation of these data structures (both array-based and linked representations) and examines classic algorithms that use these structures for tasks such as sorting, searching, and text compression. The Java programming language is used to demonstrate the topics discussed; and key notions of object-oriented programming, including encapsulation and abstract data types, are emphasized. Formerly CSCI E-119.

Prerequisite: a good working knowledge of Java (CSCI E-10b, or the equivalent).

Labs: Required sections to be arranged.
Subject:
Computer Science
CSCI E-22
Course Code Display:
CSCI E-22
Computer Science
Course Credits: 4
22
Computing Foundations for Computational Science
Course Number Integer:
205
Computing Foundations for Computational Science

Computation has long been an important tool for scientists, but the past two decades have seen a true revolution in the practice of science. Computation, in the form of both simulation and analysis, has joined theory and experimentation as the oft-quoted third pillar of science. This is an applications course highlighting the use of computers in solving scientific problems. Students are exposed to fundamental computer science concepts such as computer architectures, data structures, algorithms, and parallel computing. Students learn the fundamentals of scientific computing including abstract thinking, algorithmic development, and assessment of computational approaches. They learn to use a series of open source tools and libraries and apply them to data analysis, modeling, and visualization of real scientific problems. The course emphasizes parallel programming and parallel thinking. The recorded lectures are from the Harvard School of Engineering and Applied Sciences course Computer Science 205.

Prerequisite: CSCI E-50, or the equivalent.

Notes:
Online only, beginning Sept. 5. Optional sections to be arranged.

Subject:
Computer Science
CSCI E-205
Course Code Display:
CSCI E-205
Computer Science
Course Credits: 4
205
Discrete Mathematics for Computer Science
Course Number Integer:
20
Discrete Mathematics for Computer Science

This course covers widely applicable mathematical tools for computer science, including topics from logic, set theory, combinatorics, number theory, probability theory, and graph theory. It includes practice in reasoning formally and proving theorems. Students meet twice a week via web conference to solve problems collaboratively. They also watch recorded lectures from the Harvard School of Engineering and Applied Sciences course Computer Science 20. Formerly CSCI E-120.

Prerequisite: MATH E-15, or the equivalent.

Subject:
Computer Science
CSCI E-20
Course Code Display:
CSCI E-20
Computer Science
Course Credits: 4
20
Economics and Computation
Course Number Integer:
186
Economics and Computation

This course covers the interplay between economic thinking and computational thinking as it relates to electronic commerce, social networks, collective intelligence, and networked systems. Topics covered include game theory, peer production, reputation and recommender systems, prediction markets, crowd sourcing, network influence and dynamics, auctions and mechanisms, privacy and security, matching and allocation problems, computational social choice, and behavioral game theory. Emphasis is on core methodologies, with students engaged in theoretical, computational, and empirical exercises. The recorded lectures are from the Harvard School of Engineering and Applied Sciences course Computer Science 186.

Prerequisites: CSCI E-22, CSCI E-50, and STAT E-100, or the equivalents, with grades of B+ or higher. ECON E-1010 recommended but not required.

Notes:
Online only, beginning Jan. 29. Optional sections to be arranged.

Subject:
Computer Science
CSCI E-186
Course Code Display:
CSCI E-186
Computer Science
Course Credits: 4
186
Machine Learning
Course Number Integer:
181
Machine Learning

This course is an introduction to artificial intelligence, focusing on problems of perception, machine learning, and reasoning under uncertainty; supervised learning algorithms; ensemble learning and boosting; neural networks, multilayer perceptrons, and applications; support vector machines and kernel methods; clustering and unsupervised learning; probabilistic methods, parametric and non-parametric density estimation; maximum likelihood and maximum a posteriori estimates; Bayesian networks and graphical models; representation, inference, and learning; hidden Markov models; and computational learning theory. The recorded lectures are from the Harvard School of Engineering and Applied Sciences course Computer Science 181.

Prerequisites: CSCI E-51, CSCI E-121, and STAT E-150, or the equivalent.

Notes:
Online only, beginning Jan. 28. Optional sections to be arranged.

Subject:
Computer Science
CSCI E-181
Course Code Display:
CSCI E-181
Computer Science
Course Credits: 4
181
Visualization
Course Number Integer:
171
Visualization

The amount and complexity of information produced in science, engineering, business, and everyday human activity is increasing at staggering rates. The goal of this course is to expose students to visual representation methods and techniques that facilitate the understanding of complex data. Good visualizations not only present a visual interpretation of data, but improve comprehension, communication, and decision making. The course covers how the human visual system processes and perceives images, good design practices for visualization, how to use existing tools to make visualizations, collecting data from web sites with Python, and programming interactive web-based visualizations using JavaScript and D3. The recorded lectures are from the Harvard School of Engineering and Applied Sciences course Computer Science 171. Formerly CSCI E-64.

Prerequisite: students are expected to have programming experience (e.g. CSCI E-50); exceptions by permission of the instructor.

Notes:
Online only, beginning Jan. 29. Optional sections to be arranged.

Subject:
Computer Science
CSCI E-171
Course Code Display:
CSCI E-171
Computer Science
Course Credits: 4
171
Web Development Using XML
Course Number Integer:
18
Web Development Using XML

Students learn key XML technologies (XML, XPath, XSL, XML Schema, RNG, DTD, XQuery, DOM) as well as specific markup languages relevant to website development (XHTML, XHTMI Mobile Profile, RSS, RDF, XSL-FO, SVG, DocBook, OOXML, OpenDocument, XForms). In addition, the course covers topics such as XML and databases (native XML databases and RDBMS), XML programming APIs (DOM and SAX), Apache Cocoon (an open source XML publishing framework), eXist (an open source native XML database), and the role of XML in Web 2.0 to deliver data and functionality through Ajax and web services (SOAP and REST). Using these technologies, students develop dynamic, data-driven websites that are capable of delivering content in a variety of media formats (screen, text, print, graphics) to a variety of devices (desktop, handheld, mobile) for a variety of audiences. Formerly CSCI E-153.

Prerequisite: CSCI E-12, or the equivalent experience.

Labs: Optional sections to be arranged.
Subject:
Computer Science
CSCI E-186
Course Code Display:
CSCI E-186
Computer Science
Course Credits: 4
186

Machine Learning
Course Number Integer:
181
Machine Learning

This course is an introduction to artificial intelligence, focusing on problems of perception, machine learning, and reasoning under uncertainty; supervised learning algorithms; ensemble learning and boosting; neural networks, multilayer perceptrons, and applications; support vector machines and kernel methods; clustering and unsupervised learning; probabilistic methods, parametric and non-parametric density estimation; maximum likelihood and...

activity stream

No activity in the last 60 days.