Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

IS 2010 Curriculum Guidelines for Undergraduate Degree Programs in Information Systems Association for Computing Machinery (ACM) Association for Information Systems (AIS)

It represents a universally applicable attitude and skill set everyone, not just computer scientists, would be eager to learn and use.

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Computational Thinking 計算論的思考

Flow monitoring has become a prevalent method for monitoring traffic in high-speed networks By focusing on the analysis of flows, rather than individual packets, it is often said to be more scalable than traditional packet-based traffic analysis Flow monitoring embraces the complete chain of packet observation, flow export using protocols such as NetFlow and IPFIX, data collection, and data analysis In contrast to what is often assumed, all stages of flow monitoring are closely intertwined Each of these stages therefore has to be thoroughly understood, before being able to perform sound flow measurements Otherwise, flow data artifacts and data loss can be the consequence, potentially without being observed This paper is the first of its kind to provide an integrated tutorial on all stages of a flow monitoring setup As shown throughout this paper, flow monitoring has evolved from the early 1990s into a powerful tool, and additional functionality will certainly be added in the future We show, for example, how the previously opposing approaches of deep packet inspection and flow monitoring have been united into novel monitoring approaches

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Flow Monitoring Explained: From Packet Capture to Data Analysis With NetFlow and IPFIX

Disclosed is a computer network node discovery system (120) that provides a general way of discovering network elements, or nodes (100), connected to a computer network (118), and a specific algorithm for discovering nodes connected to a TCP/IP network, using the SNMP protocol available within the TCP/IP network software. Some nodes on a network, called discovery agents, can convey knowledge of the existence of other nodes on the network. The network discovery system queries these agents and obtains the information they have about other nodes on the network. It then queries each of the nodes obtained to determine if that node is also a discovery agent. In this manner, most of the nodes on a network can be discovered. The process of querying discovery agents to obtain a list of nodes known to the discovery agents is repeated at timed intervals to obtain information about nodes that are not always active. In a TCP/IP network, discovery agents are nodes that respond to queries for an address translation table which translates internet protocol (IP) addresses to physical addresses. The data from each node's address translation table is used to obtain both the IP and the physical address of other nodes on the network. These nodes are then queried to obtain additional information. After all the nodes on a network are discovered, the list of nodes is written to a database where it can be displayed by the network manager or other users of the network.

Automatic discovery of network elements

Computer Science Curriculum 2008: An Interim Revision of CS 2001

The National Science Digital Library (NSDL), launched in December 2002, is emerging as a center of innovation in digital libraries as applied to education. As a part of this extensive project, the GetSmart system was created to apply knowledge management techniques in a learning environment. The design of the system is based on an analysis of learning theory and the information search process. Its key notion is the integration of search tools and curriculum support with concept mapping. More than 100 students at the University of Arizona and Virginia Tech used the system in the fall of 2002. A database of more than one thousand student-prepared concept maps has been collected with more than forty thousand relationships expressed in semantic, graphical, node-link representations. Preliminary analysis of the collected data is revealing interesting knowledge representation patterns.

Convergence of knowledge management and e-learning: the GetSmart experience

This session reports on a workshop convened by the ACM Education Board with funding by the US National Science Foundation and invites discussion from the community on the workshop findings. The topic, curricular directions for cybersecurity, is one that resonates in many departments considering how best to prepare graduates to face the challenges of security issues in employment and future research. The session will include presentation of the workshop context and conclusions, but will be open to participant discussion. This will be the first public presentation of the results of the workshop and the first opportunity for significant response.

Toward curricular guidelines for cybersecurity

An overview is presented of the statistical sampling of measurements from a computer network, a necessary task in all five facilities of the proposed ISO network management architecture. Network measurement, viewed from the standpoint of real-time status reporting, is performed either to approximate the condition of a network parameter over a given interval or to detect a significant change in system behavior over consecutive intervals. Appropriate statistical tools for sampling and analyzing traffic in real time are presented. >

Management of sampled real-time network measurements

A syllabus defines the contents of a course, as well as other information such as resources and assignments. In this paper, we report on our work towards creating a syllabus repository of Computer Science courses across universities in the USA. We present some statistics from our initial collection of 8000+ syllabi. We show a syllabus creator that is integrated with Moodle [5] moodle_2006_moodle, an open-source course management system, which allows for the creation of a syllabus for a particular course. Among other information, it includes knowledge units from the Computing Curricula 2001 body of knowledge. The goal of the syllabus repository is to provide added value to the Computer Science Education community, and we present some such offerings. We conclude by presenting our future plans for the syllabus repository. These include using automated techniques to collect and classify syllabi, providing recommendations to instructors when creating a syllabus, and allowing the community to share their syllabi automatically. The syllabus collection will be part of the Computing and Information Technology Interactive Digital Educational Library (CITIDEL), a collection of the National Science Digital Library (NSDL).

Lillian N. Cassel

Papers

Computer Science Curriculum 2008: An Interim Revision of CS 2001

Convergence of knowledge management and e-learning: the GetSmart experience

Toward curricular guidelines for cybersecurity

Management of sampled real-time network measurements

Towards a syllabus repository for computer science courses