The entire program requires no less than 30 semester hours of course work. This includes the Computer Science core courses and the completion of the thesis or non-thesis option. Computer Science and Computer Engineering courses at the level and above can be used toward your degree Sep 17, · The Master of Computer Science in Data Science (MCS-DS) track is a non-thesis (coursework-only) program of study that leads to the MCS degree using courses that focus on data science. The MCS-DS track requires 32 credit hours of graduate coursework, completed through eight graduate-level courses each at the four credit hour level Sep 27, · Duke's credit master's in computer science offers thesis and coursework-only options. Both plans require an oral exam. Thesis option enrollees complete a faculty-supervised research project, written report, and public defense. Learners following the coursework-only plan submit a portfolio for review
Top 50 Master's in Computer Science Degree Programs (Updated) - Computer Science Degree Hub
Dernières mises à jour en lien avec la COVID disponibles ici. Latest information about COVID available here. Offered by: Computer Science Faculty of Science. Instructors: Maheswaran, Muthucumaru Fall Maheswaran, Muthucumaru Winter. Computer Science Sci : Exposure to ongoing research directions in computer science through regular attendance of the research colloquium organized by the School of Computer Science. Complementary courses must satisfy a Computer Science breadth requirement, with at least one course in two of the Theory, Systems, and Application areas.
Areas covered by specific courses are determined by the Computer Science graduate program director. Computer Science Sci : State-of-the-art language-based techniques for enforcing security policies in distributed computing environments. Static techniques such as type- and proof-checking technologyverification of security policies and applications such as proof-carrying code, certifying compilers, and proof-carrying authentication.
Prerequisites: COMPCOMP Computer Science Sci : Propositional logic - syntax and semantics, temporal logic, other modal logics, model checking, symbolic model checking, binary decision diagrams, other approaches to formal verification.
Terms: This course is not scheduled for the academic year. Instructors: There are no professors associated with this course for the academic year. Prerequisites: COMP and COMP Computer Science Sci : Introduction to modern constructive logic, its mathematical properties, and its numerous applications in computer science.
Restriction: Not open to students who have taken COMP Computer Science Sci : Models for sequential and parallel computations: Turing machines, boolean circuits. The equivalence of various models and the Church-Turing thesis. Unsolvable problems. Model dependent measures of computational complexity.
Abstract complexity theory. Exponentially and super-exponentially difficult problems. Complete problems. Computer Science Sci : Designing and programming reliable numerical algorithms. Stability of algorithms and condition of problems.
Reliable and efficient algorithms for solution of equations, linear least squares problems, the singular value decomposition, the eigenproblem and related problems.
Perturbation analysis of problems. Algorithms for structured matrices. Prerequisite: MATH or COMP Computer Science Sci : This course presents an in-depth study of modern cryptography and data security. The basic information theoretic and computational properties of classical and modern cryptographic systems are presented, followed by a cryptanalytic examination of several important systems.
We will study the applications of cryptography to the security of systems. Prerequisites: COMP or COMPMATH Computer Science Sci : Algorithmic and structural master thesis of computer science in combinatorial optimization with a focus upon theory and applications. Topics include: polyhedral methods, network optimization, the ellipsoid method, graph algorithms, matroid theory and submodular functions.
Prerequisite: Math or COMP or equivalent. Restriction: This course is reserved for undergraduate honours students and graduate students. Not open to students who have taken or master thesis of computer science taking MATH Computer Science Sci : Foundations of game theory.
Computation aspects of equilibria. Theory of auctions and modern auction design. General equilibrium theory and welfare economics. Algorithmic mechanism design. Dynamic games. Prerequisite: COMP or MATH or MATH or MATHor instructor permission. Restriction: Not open to students who are taking or have taken MATH Computer Science Sci : The theory and application of approximation algorithms. Topics include: randomized algorithms, network optimization, linear programming and semi definite programming techniques, the game theoretic method, the primal-dual method, and metric embeddings.
Prerequisites: COMP or MATH or permission of instructor. Computer Science Sci : Algorithms for connectivity, master thesis of computer science, partitioning, clustering, colouring and matching.
Isomorphism testing. Algorithms for special classes of graphs. Layout and embedding algorithms for graphs and networks. Prerequisite: COMP or COMP or MATH Computer Science Sci : Concentration inequalities, PAC model, VC dimension, Rademacher complexity, convex optimization, gradient descent, boosting, kernels, support vector machines, regression and learning bounds, master thesis of computer science.
Further topics selected from: Gaussian processes, online learning, regret bounds, basic neural network theory. Prerequisites: MATH or COMP or COMPMATHMATH and MATH or ECSE Restrictions: Not open to students who have taken or are taking MATH Not open to students who have taken COMP when the topic was "Statistical Learning Theory" or "Mathematical Topics for Machine Learning".
Not open to students who have taken COMP when the topic was "Mathematical Foundations of Machine Learning". Computer Science Sci : Use of computer in solving problems in discrete optimization. Linear programming and extensions. Network simplex method, master thesis of computer science. Applications of linear programming.
Vertex enumeration. Geometry of linear programming. Implementation issues and robustness. Students will do a project on an application of their choice. Prerequisites: COMP and MATH Computer Science Sci : Formulation, solution and applications of integer programs. Branch and bound, cutting plane, and column generation algorithms. Combinatorial optimization. Polyhedral methods, master thesis of computer science. A large emphasis will be placed on modelling. Students will select and present a case study of an application of integer programming in an area of their choice.
Prerequisites: COMP or MATH Computer Science Sci : Study of elementary data structures: lists, stacks, queues, trees, hash tables, binary search trees, red-black trees, heaps. Augmenting data structures. Sorting and selection, Recursive algorithms. Advanced data structures including binomial heaps, Fibonacci heaps, disjoint set structures, and splay trees. String algorithms. Huffman trees and suffix trees. Graph algorithms. Computer Science Sci : Introduction to mathematical concepts important across computer science, how to think mathematically, and how to write proofs.
Master thesis of computer science techniques such as induction, contradiction, and monovariants; topics in combinatorics, graph theory, algebra, analysis, and probability; mathematical analysis of algorithms, data structures, and computational complexity.
Emphasis on the mathematical explanations for useful concepts. Restrictions: Not open master thesis of computer science students who have majored in Mathematics or an equivalent subject, or have taken a proof-based math or computer science course within the previous two years.
Not open to students who have taken COMP when the topic was "Mathematical Tools for Computer Science". Computer Science Sci : Efficient and reliable numerical algorithms in estimation and their applications. Linear models and least squares estimation.
Maximum-likelihood estimation. Kalman filtering, master thesis of computer science. Adaptive estimation, GPS measurements and mathematical models for positioning. Position estimation. Fault detection and exclusion. Prerequisites: MATHMATH and COMP Computer Science Sci : Information theoretic definitions of security, zero-knowledge protocols, secure function evaluation protocols, master thesis of computer science, cryptographic primitives, privacy amplification, error correction, quantum cryptography, quantum cryptanalysis.
Computer Science Sci : Review of the basic notions of cryptography and quantum information theory. Quantum key distribution and its proof of security.
Best Master Thesis in Computer Science 2014
, time: 2:20Master of Science Program | Computer Science and Engineering
Sep 17, · The Master of Computer Science in Data Science (MCS-DS) track is a non-thesis (coursework-only) program of study that leads to the MCS degree using courses that focus on data science. The MCS-DS track requires 32 credit hours of graduate coursework, completed through eight graduate-level courses each at the four credit hour level The entire program requires no less than 30 semester hours of course work. This includes the Computer Science core courses and the completion of the thesis or non-thesis option. Computer Science and Computer Engineering courses at the level and above can be used toward your degree The Master of Science in Computer Science (M.S. CS) program is a terminal degree program designed to prepare students for more highly productive careers in industry. Graduates receive the MSCS for completing one of three options in the program as described in the program of study. The program is
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