التنقيب في البيانات جامعة الأميرة نورة

هذه المجموعة تعنى بالموارد التعليمية الخاصة بالتنقيب عن البيانات.
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All resources in التنقيب في البيانات جامعة الأميرة نورة

تنقيب في البيانات

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التنقيب في البيانات(بالانجليزية: data mining) هي عملية بحث محوسب ويدوي عن معرفة من البيانات دون فرضيات مسبقة عما يمكن أن تكون هذه المعرفة. كما ويعرف التنقيب في البيانات على أنه عملية تحليل كمية بيانات (عادة ما تكون كمية كبيرة) لإيجاد علاقة منطقية تلخص البيانات بطريقة جديدة تكون مفهومة ومفيدة لصاحب البيانات. يطلق اسم "نماذج" models على العلاقات والبيانات الملخصة التي يتم الحصول عليها من التنقيب في البيانات. يتعامل تنقيب البيانات عادة مع بيانات يكون قد تم الحصول عليها بغرض غير غرض التنقيب في البيانات (مثلاً قاعدة بيانات التعاملات في مصرف ما) مما يعني أن طريقة التنقيب في البيانات لاتؤثر مطلقاً على طريقة تجميع البيانات ذاتها. هذه هي أحد النواحي التي يختلف فيها التنقيب في البيانات عن الإحصاء، ولهذا يشار إلى عملية التنقيب في البيانات على أنها عملية إحصائية ثانوية. يشير التعريف أيضاً إلى أن كمية البيانات تكون عادة كبيرة، أما في حال كون كمية البيانات صغيرة فيفضل استخدام الطرق الإحصائية العادية في تحليلها.

Material Type: Reading

Author: Dalal Alqahtani

Date created : 1-جمادي الآخرة-1438

معادلة جديدة للذكاء

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هل يوجد معادلة للذكاء؟ نعم. إنها F = T ∇ Sτ. في حديث مذهل و مثقف، يشرح ألكس ويسنر-جروس الفيزيائي و عالم الكمبيوتر ما الذي يعنيه ذلك بحق السماء. (تم تصويره في تيد X بيكون ستريت)

Author: Alex Wissner-Gross

Date created : 7-ربيع الأول-1438

Pattern Recognition and Analysis, Fall 2006

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Fundamentals of characterizing and recognizing patterns and features of interest in numerical data. Basic tools and theory for signal understanding problems with applications to user modeling, affect recognition, speech recognition and understanding, computer vision, physiological analysis, and more. Decision theory, statistical classification, maximum likelihood and Bayesian estimation, non-parametric methods, unsupervised learning and clustering. Additional topics on machine and human learning from active research.

Material Type: Full Course, Textbook

Author: Picard, Rosalind

Date created : 1-ذو الحجة-1426

Topics in Brain and Cognitive Sciences Human Ethology, Spring 2001

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Survey and special topics designed for graduate students in the brain and cognitive sciences. Emphasizes ethological studies of natural behavior patterns and their analysis in laboratory work, with contributions from field biology (mammology, primatology), sociobiology, and comparative psychology. Stresses human behavior but also includes major contributions from studies of other vertebrates and of invertebrates.

Material Type: Full Course, Textbook

Author: Schneider, Gerald

Date created : 6-شوال-1421

Natural Language and the Computer Representation of Knowledge, Spring 2003

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Relationship between computer representation of knowledge and the structure of natural language. Emphasizes development of the analytical skills necessary to judge the computational implications of grammatical formalisms, and uses concrete examples to illustrate particular computational issues. Efficient parsing algorithms for context-free grammars; augmented transition network grammars. Question answering systems. Extensive laboratory work on building natural language processing systems. 6.863 is a laboratory-oriented course on the theory and practice of building computer systems for human language processing, with an emphasis on the linguistic, cognitive, and engineering foundations for understanding their design.

Material Type: Full Course, Textbook

Author: Berwick, Robert

Date created : 28-شوال-1423

Advanced Natural Language Processing, Fall 2005

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This course is a graduate introduction to natural language processing - the study of human language from a computational perspective. It covers syntactic, semantic and discourse processing models, emphasizing machine learning or corpus-based methods and algorithms. It also covers applications of these methods and models in syntactic parsing, information extraction, statistical machine translation, dialogue systems, and summarization. The subject qualifies as an Artificial Intelligence and Applications concentration subject.

Material Type: Full Course, Textbook

Authors: Barzilay, Regina, Collins, Michael

Date created : 20-ذو القعدة-1425

Genomic Medicine, Spring 2004

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This course reviews the key genomic technologies and computational approaches that are driving advances in prognostics, diagnostics, and treatment. Throughout the semester, emphasis will return to issues surrounding the context of genomics in medicine including: what does a physician need to know? what sorts of questions will s/he likely encounter from patients? how should s/he respond? Lecturers will guide the student through real world patient-doctor interactions. Outcome considerations and socioeconomic implications of personalized medicine are also discussed. The first part of the course introduces key basic concepts of molecular biology, computational biology, and genomics. Continuing in the informatics applications portion of the course, lecturers begin each lecture block with a scenario, in order to set the stage and engage the student by showing: why is this important to know? how will the information presented be brought to bear on medical practice? The final section presents the ethical, legal, and social issues surrounding genomic medicine. A vision of how genomic medicine relates to preventative care and public health is presented in a discussion forum with the students where the following questions are explored: what is your level of preparedness now? what challenges must be met by the healthcare industry to get to where it needs to be?

Material Type: Full Course, Textbook

Author: Kohane, Isaac

Date created : 9-ذو القعدة-1424

Medical Decision Support, Fall 2005

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Presents the main concepts of decision analysis, artificial intelligence, and predictive model construction and evaluation in the specific context of medical applications. Emphasizes the advantages and disadvantages of using these methods in real-world systems and provides hands-on experience. Technical focus on decision analysis, knowledge-based systems (qualitative and quantitative), learning systems (including logistic regression, classification trees, neural networks), and techniques to evaluate the performance of such systems. Students produce a final project using the methods learned in the subject, based on actual clinical data. (Required for students in the Master's Program in Medical Informatics, but open to other graduate students and advanced undergraduates.)

Material Type: Full Course, Textbook

Date created : 20-ذو القعدة-1425

Great Ideas in Theoretical Computer Science, Spring 2008

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This course provides a challenging introduction to some of the central ideas of theoretical computer science. It attempts to present a vision of "computer science beyond computers": that is, CS as a set of mathematical tools for understanding complex systems such as universes and minds. Beginning in antiquity--with Euclid's algorithm and other ancient examples of computational thinking--the course will progress rapidly through propositional logic, Turing machines and computability, finite automata, GĚŚdel's theorems, efficient algorithms and reducibility, NP-completeness, the P versus NP problem, decision trees and other concrete computational models, the power of randomness, cryptography and one-way functions, computational theories of learning, interactive proofs, and quantum computing and the physical limits of computation. Class participation is essential, as the class will include discussion and debate about the implications of many of these ideas.

Material Type: Full Course, Textbook

Author: Aaronson, Scott

Date created : 22-ذو الحجة-1428

Bioinformatics and Proteomics, January (IAP) 2005

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This interdisciplinary course provides a hands-on approach to students in the topics of bioinformatics and proteomics. Lectures and labs cover sequence analysis, microarray expression analysis, Bayesian methods, control theory, scale-free networks, and biotechnology applications. Designed for those with a computational and/or engineering background, it will include current real-world examples, actual implementations, and engineering design issues. Where applicable, engineering issues from signal processing, network theory, machine learning, robotics and other domains will be expounded upon.

Material Type: Full Course, Textbook

Author: Gil, Alterovitz

Date created : 20-ذو القعدة-1425

Relational Machines, Spring 2005

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This course examines the issues, principles, and challenges toward building relational machines through a combination of studio-style design and critique along with lecture, lively discussion of course readings, and assignments. Insights from social psychology, human-computer interaction, and design will be examined, as well as how these ideas are manifest in a broad range of applications for software agents and robots.

Material Type: Full Course, Textbook

Author: Breazeal, Cynthia

Date created : 20-ذو القعدة-1425

Algorithms for Computational Biology, Spring 2005

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This course is offered to undergraduates and addresses several algorithmic challenges in computational biology. The principles of algorithmic design for biological datasets are studied and existing algorithms analyzed for application to real datasets. Topics covered include: biological sequence analysis, gene identification, regulatory motif discovery, genome assembly, genome duplication and rearrangements, evolutionary theory, clustering algorithms, and scale-free networks.

Material Type: Full Course, Textbook

Author: Kellis, Manolis

Date created : 20-ذو القعدة-1425

Machine Learning, Fall 2006

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Principles, techniques, and algorithms in machine learning from the point of view of statistical inference; representation, generalization, and model selection; and methods such as linear/additive models, active learning, boosting, support vector machines, hidden Markov models, and Bayesian networks.

Material Type: Full Course, Textbook

Author: Jaakkola, Tommi

Date created : 1-ذو الحجة-1426

Computational Models of Discourse, Spring 2004

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This course is a graduate level introduction to automatic discourse processing. The emphasis will be on methods and models that have applicability to natural language and speech processing. The class will cover the following topics: discourse structure, models of coherence and cohesion, plan recognition algorithms, and text segmentation. We will study symbolic as well as machine learning methods for discourse analysis. We will also discuss the use of these methods in a variety of applications ranging from dialogue systems to automatic essay writing. This subject qualifies as an Artificial Intelligence and Applications concentration subject.

Material Type: Full Course, Textbook

Author: Regina Barzilay

Date created : 9-ذو القعدة-1424

Artificial Intelligence, Fall 2010

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This course introduces students to the basic knowledge representation, problem solving, and learning methods of artificial intelligence. Upon completion of 6.034, students should be able to develop intelligent systems by assembling solutions to concrete computational problems, understand the role of knowledge representation, problem solving, and learning in intelligent-system engineering, and appreciate the role of problem solving, vision, and language in understanding human intelligence from a computational perspective.

Material Type: Full Course, Textbook

Author: Winston, Patrick Henry

Date created : 15-محرم-1431

Principles of Autonomy and Decision Making, Fall 2010

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This course surveys a variety of reasoning, optimization and decision making methodologies for creating highly autonomous systems and decision support aids. The focus is on principles, algorithms, and their application, taken from the disciplines of artificial intelligence and operations research. Reasoning paradigms include logic and deduction, heuristic and constraint-based search, model-based reasoning, planning and execution, and machine learning. Optimization paradigms include linear programming, integer programming, and dynamic programming. Decision-making paradigms include decision theoretic planning, and Markov decision processes.

Material Type: Full Course, Textbook

Authors: Frazzoli, Emilio, Williams, Brian

Date created : 26-محرم-1432

Computational Cognitive Science, Fall 2004

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This course is an introduction to computational theories of human cognition. Drawing on formal models from classic and contemporary artificial intelligence, students will explore fundamental issues in human knowledge representation, inductive learning and reasoning. What are the forms that our knowledge of the world takes? What are the inductive principles that allow us to acquire new knowledge from the interaction of prior knowledge with observed data? What kinds of data must be available to human learners, and what kinds of innate knowledge (if any) must they have?

Material Type: Full Course, Textbook

Author: Tenenbaum, Joshua

Date created : 9-ذو القعدة-1424

Artificial Intelligence

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This course includes materials on AI programming, logic, search, game playing, machine learning, natural language understanding, and robotics, which will introduce the student to AI methods, tools, and techniques, their application to computational problems, and their contribution to understanding intelligence. The material is introductory; the readings cite many resources outside those assigned in this course, and students are encouraged to explore these resources to pursue topics of interest. Upon successful completion of this course, the student will be able to: Describe the major applications, topics, and research areas of artificial intelligence (AI), including search, machine learning, knowledge representation and inference, natural language processing, vision, and robotics; Apply basic techniques of AI in computational solutions to problems; Discuss the role of AI research areas in growing the understanding of human intelligence; Identify the boundaries of the capabilities of current AI systems. (Computer Science 405)

Material Type: Full Course, Reading, Syllabus, Textbook

Date created : 20-ذو الحجة-1432

Advanced Artificial Intelligence

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This course will present advanced topics in Artificial Intelligence (AI), including inquiries into logic, artificial neural network and machine learning, and the Turing machine. Upon successful completion of this course, students will be able to: define the term 'intelligent agent,' list major problems in AI, and identify the major approaches to AI; translate problems into graphs and encode the procedures that search the solutions with the graph data structures; explain the differences between various types of logic and basic statistical tools used in AI; list the different types of learning algorithms and explain why they are different; list the most common methods of statistical learning and classification and explain the basic differences between them; describe the components of Turing machine; name the most important propositions in the philosophy of AI; list the major issues pertaining to the creation of machine consciousness; design a reasonable software agent with java code. (Computer Science 408)

Material Type: Full Course

Date created : 20-ذو الحجة-1432

Statistical Learning Theory and Applications, Spring 2006

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This course focuses on the problem of supervised learning from the perspective of modern statistical learning theory starting with the theory of multivariate function approximation from sparse data. It develops basic tools such as Regularization including Support Vector Machines for regression and classification. It derives generalization bounds using both stability and VC theory. It also discusses topics such as boosting and feature selection and examines applications in several areas: Computer Vision, Computer Graphics, Text Classification and Bioinformatics. The final projects and hands-on applications and exercises are planned, paralleling the rapidly increasing practical uses of the techniques described in the subject.

Material Type: Full Course, Textbook

Author: Poggio, Tomaso

Date created : 1-ذو الحجة-1426