As a Ph. Coursework revolves around seven core groups: theory of computation, systems, programming languages, networks and security, databases, software engineering, and computational intelligence. The amount of coursework is dependent on previous degrees earned. Passing a written and oral qualifying exam, comprehensive exam, and defending a thesis is expected.
A thesis research problem must be presented and defended to a committee of faculty at the comprehensive exam. A growing number of tech companies are calling Chicago home. The city government is extremely open with its data, allowing and encouraging unusual access for unique research opportunities.
Coursework explores seven core groups: theory of computation, systems, programming languages, networks and security, databases, software engineering, and computational intelligence. A research career in academia is attainable with a Ph.
View Details. Skip to main site navigation Skip to main content. Computer Science Ph. Program Type Doctoral. Degree Ph. Department Computer Science. College College of Computing. Pursue an academic or industrial research career through this doctoral program.
Program Overview Coursework explores seven core groups: theory of computation, systems, programming languages, networks and security, databases, software engineering, and computational intelligence.
Career Opportunities A research career in academia is attainable with a Ph. Computer scientist Computer systems engineer Computer science professor Computer network architect Research and development Curriculum View Details. Admission Requirements The minimum standards for admission to the computer science Ph. Applicants must have a high GPA. GRE scores. For the Direct program, the minimum required GRE scores are and 4.List of famous essay writers
The score should be at most 5-years-old. Proficiency in English. For applicants with degrees from schools where the primary language of instruction was not English, a minimum score of 70 on the internet-based TOEFL, 47 on the PTE, or 5.The philosophy of computer science is concerned with those ontological, methodological, and ethical issues that arise from within the academic discipline of computer science as well as from the practice of software development.
Thus, the philosophy of computer science shares the same philosophical goals as the philosophy of mathematics and the many subfields of the philosophy of science, such as the philosophy of biology or the philosophy of the social sciences. The philosophy of computer science also considers the analysis of computational artifactsthat is, human-made computing systems, and it focuses on methods involved in the design, specification, programming, verification, implementation, and testing of those systems.
The abstract nature of computer programs and the resulting complexity of implemented artifacts, coupled with the technological ambitions of computer science, ensures that many of the conceptual questions of the philosophy of computer science have analogues in the philosophy of mathematicsthe philosophy of empirical sciences, and the philosophy of technology. Other issues characterize the philosophy of computer science only.
We shall concentrate on three tightly related groups of topics that form the spine of the subject. First we discuss topics related to the ontological analysis of computational artifacts, in Sections 1—5 below. Second, we discuss topics involved in the methodology and epistemology of software development, in Sections 6—9 below. Third, we discuss ethical issues arising from computer science practice, in Section 10 below.
Applications of computer science are briefly considered in section Computational artifacts underpin our Facebook pages, control air traffic around the world, and ensure that we will not be too surprised when it snows. They have been applied in algebra, car manufacturing, laser surgery, banking, gastronomy, astronomy, and astrology. Indeed, it is hard to find an area of life that has not been fundamentally changed and enhanced by their application.
But what is it that is applied?How to get started writing
What are the things that give substance to such applications? The trite answer is the entities that computer scientists construct, the artifacts of computer science, computational artifacts, if you will. Much of the philosophy of computer science is concerned with their nature, specification, design, and construction.
Folklore has it that computational artifacts fall into two camps: hardware and software. Presumably, software includes compilers and natural language understanding systems, whereas laptops and tablets are hardware. But how is this distinction drawn: How do we delineate what we take to be software and what we take to be hardware?The goal of this project is to better understand deep learning by drawing on insights from philosophy of science.
Deep learning algorithms have emerged in recent years at the forefront of machine learning. They are a new, powerful technology with many applications in both science and everyday life, ranging from data analysis in particle physics to mastering the game of GO.
While deep learning algorithms are successfully applied in many areas, they are not yet well understood: There is, first, a lack of "theory of deep learning", which means that many mathematical properties of these models are not known; second, there is a lack of "interpretability", which means that humans do not understand how these models achieve their goals.
Computer scientists are well aware of both lacunae and have called for more research to fill them. But it is far from clear what this means. To contribute to a better understanding of deep learning, this project carries out interdisciplinary research at the borderline between computer science and philosophy.
Specifically, we analyze theoretical work on understanding in deep learning, such as the Information Bottleneck method, and we connect them to the philosophical debates on scientific and mathematical explanation and understanding. We then use this work to clarify the ongoing debate on interpretability and deep learning theory in computer science. Thus, by moving back and forth between computer science and philosophy, we establish a firm connection between deep learning and philosophy of science, and we thereby contribute to a better understanding of this important new technology in society.
This project is funded by the cogito foundation. Homepage Studies Research About us. Portal UniBE. Search Search Search. Claus Beisbart Funktion Professor extraordinarius with focus on philosophy of science Mail claus.Information science also known as information studies is an academic field which is primarily concerned with analysis, collection, classificationmanipulation, storage, retrievalmovement, dissemination, and protection of information.
Historically, information science is associated with computer sciencepsychologytechnology and intelligence agencies. Information science focuses on understanding problems from the perspective of the stakeholders involved and then applying information and other technologies as needed. In other words, it tackles systemic problems first rather than individual pieces of technology within that system.3.1 Ontology in Philosophy and Computer Science
In this respect, one can see information science as a response to technological determinismthe belief that technology "develops by its own laws, that it realizes its own potential, limited only by the material resources available and the creativity of its developers.
It must therefore be regarded as an autonomous system controlling and ultimately permeating all other subsystems of society. Many universities have entire colleges, departments or schools devoted to the study of information science, while numerous information-science scholars work in disciplines such as communicationcomputer sciencelawand sociology.
Several institutions have formed an I-School Caucus see List of I-Schoolsbut numerous others besides these also have comprehensive information foci. Within information science, current issues as of [update] include:. The first known usage of the term "information science" was in Some authors use informatics as a synonym for information science. This is especially true when related to the concept developed by A. Mikhailov and other Soviet authors in the mids.
The Mikhailov school saw informatics as a discipline related to the study of scientific information. Definitions reliant on the nature of the tools used for deriving meaningful information from data are emerging in Informatics academic programs. Regional differences and international terminology complicate the problem.
Some people [ which? For example, when library scientists began also to use the phrase "Information Science" to refer to their work, the term "informatics" emerged:. Another term discussed as a synonym for "information studies" is " information systems ". Philosophy of information studies conceptual issues arising at the intersection of computer scienceinformation technologyand philosophy.
It includes the investigation of the conceptual nature and basic principles of informationincluding its dynamics, utilisation and sciences, as well as the elaboration and application of information-theoretic and computational methodologies to its philosophical problems.
In science and information science, an ontology formally represents knowledge as a set of concepts within a domainand the relationships between those concepts.
It can be used to reason about the entities within that domain and may be used to describe the domain. More specifically, an ontology is a model for describing the world that consists of a set of types, properties, and relationship types. Exactly what is provided around these varies, but they are the essentials of an ontology. There is also generally an expectation that there be a close resemblance between the real world and the features of the model in an ontology.
In theory, an ontology is a "formal, explicit specification of a shared conceptualisation". Ontologies are the structural frameworks for organizing information and are used in artificial intelligencethe Semantic Websystems engineeringsoftware engineeringbiomedical informaticslibrary scienceenterprise bookmarkingand information architecture as a form of knowledge representation about the world or some part of it.
The creation of domain ontologies is also fundamental to the definition and use of an enterprise architecture framework.The PDF includes all information on this page and its related tabs. Subject course information includes any changes approved for the current academic year.
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Please note that there may be no data available if the number of course participants is very small. Artificial intelligence AIlogic, robotics, virtual reality: fascinating areas where computer science and philosophy meet. The two disciplines share a broad focus on the representation of information and rational inference, embracing common interests in algorithms, cognition, intelligence, language, models, proof and verification.
Computer scientists need to be able to reflect critically and philosophically as they push forward into novel domains, while philosophers need to understand a world increasingly shaped by technology in which a whole new range of enquiry has opened up, from the philosophy of AI to the ethics of privacy and intellectual property.
Some of the greatest thinkers of the past — including Aristotle, Hobbes and Turing — dreamed of automating reasoning and what this might achieve; the computer has now made it a reality, providing a wonderful tool for extending our speculation and understanding. The study of philosophy develops analytical, critical and logical rigour, and the ability to think through the consequences of novel ideas and speculations.
It stretches the mind by considering a wide range of thought on subjects as fundamental as the limits of knowledge, the nature of reality and our place in it, and the basis of morality. Computer science is about understanding computer systems at a deep level. Computers and the programs they run are among the most complex products ever created. Designing and using them effectively presents immense challenges.
Facing these challenges is the aim of computer science as a practical discipline.
Computer Science and Philosophy
Both subjects are intellectually exciting and creative. The degree combines analytical and technical knowledge with rhetorical and literary skills, and the chance to study within two internationally acclaimed academic departments. Students do not need to choose between the three-year and four-year options when applying. Instead all students apply for the four-year course, and then decide at the start of the third year whether they wish to continue to the fourth year which is subject to achieving a at the end of the third year.
Later years include a wide range of options, with an emphasis on courses near the interface between the two subjects. The fourth year enables students to study a variety of advanced topics and complete an in-depth research project.
For the first two years, your work is divided between about ten lectures and two to three college-based tutorials each week, alongside Computer Science practical classes — usually one session a week. In the second year you will take part in a Computer Science group design practical, why may be sponsored by industry.
In your third and fourth years, Philosophy continues to be taught through tutorials, while there are classes in the department for most Computer Science courses. Most tutorials, classes, and lectures are delivered by staff who are tutors in their subject. Many are world-leading experts with years of experience in teaching and research. Some teaching may also be delivered by postdoctoral researchers or postgraduate students who are studying at doctorate level.
Between nine and eleven three-hour written papers, including at least two in Computer Science and at least three in Philosophy. The courses listed above are illustrative and may change.
Computer Science: written paper or take-home exam; Philosophy: three-hour written paper and 5, word essay. The content and format of this course may change in some circumstances.
Read further information about potential course changes.
Wherever possible, your grades are considered in the context in which they have been achieved. If, and only if, you have chosen to take any science A-levels, we expect you to take and pass the practical component in addition to meeting any overall grade requirement.
If English is not your first language you may also need to meet our English language requirements. The information below gives specific details for students applying for this course. Separate registration for this test is required and it is the responsibility of the candidate to ensure that they are registered.
We strongly recommend making the arrangements in plenty of time before the deadline.The philosophy of computer science is concerned with the philosophical questions that arise within the study of computer science. There is still no common understanding of the content, aim, focus, or topic of the philosophy of computer science,  despite some attempts to develop a philosophy of computer science like the philosophy of physics or the philosophy of mathematics.
Due to the abstract nature of computer programs and the technological ambitions of computer science, many of the conceptual questions of the philosophy of computer science are also comparable to the philosophy of scienceand the philosophy of technology. Many of the central philosophical questions of computer science are centered on the logical, ontological and epistemological issues that concern it. The Church—Turing thesis and its variations are central to the theory of computation. Since, as an informal notion, the concept of effective calculability does not have a formal definition, the thesis, although it has near-universal acceptance, cannot be formally proven.
The implications of this thesis is also of philosophical concern. Philosophers have interpreted the Church—Turing thesis as having implications for the philosophy of mind.
Understanding Deep Learning – Bringing Together Computer Science and Philosophy
The P versus NP problem is an unsolved problem in computer science and mathematics. It asks whether every problem whose solution can be verified in polynomial time and so defined to belong to the class NP can also be solved in polynomial time and so defined to belong to the class P. There would be no special value in "creative leaps", no fundamental gap between solving a problem and recognizing the solution once it's found.
Everyone who could appreciate a symphony would be Mozart ; everyone who could follow a step-by-step argument would be Gauss. From Wikipedia, the free encyclopedia.Help list reference resume resume services
The Science of Computing: Shaping a Discipline. Chapman Hall. Journal of Applied Logic. Stanford Encyclopedia of Philosophy. Jack November 10, In Zalta, Edward N. Philosophy of Mind: Classical and Contemporary Readings.
New York: Oxford University Press.Top admission essay editor websites for university
Gasarch June NP poll" PDF. Retrieved 26 September NP poll results". Communications of the ACM. Computer science. Computer architecture Embedded system Real-time computing Dependability. Network architecture Network protocol Network components Network scheduler Network performance evaluation Network service. Interpreter Middleware Virtual machine Operating system Software quality. Programming paradigm Programming language Compiler Domain-specific language Modeling language Software framework Integrated development environment Software configuration management Software library Software repository.
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