Hidden faces maths coursework

Contents

  1. Innovative math lessons you can use in your classroom today
  2. Reliable Coursework Writing Service From Australia
  3. Sociology coursework help: a world of modern mass media
  4. Course Information
  5. Do We Need a New Way to Teach Math?

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Innovative math lessons you can use in your classroom today

The emphasis will be on developing JavaScript programs that run in the browser. This course is most useful for students who have some programming and web development experience and want to develop moderate JavaScript skills to be able to build complex, interactive applications in the browser. Rust is a systems programming language that runs blazingly fast, prevents segfaults, and guarantees thread safety.

Topics covered include: the Rust type system traits, generics , memory management move semantics, borrowing, and lifetimes , functional programming closures, higher order functions, ADTs , parallelism and concurrency, Rust for the web WASM. Evaluation is based on regular homework assignments as well as a final project and class participation. Prerequisite s : The course assumes a basic familiarity with computer programming. To the extent possible, the projects will be done in Python and Solidity. Blockchain or Distributed Ledger Technology DLT provides a decentralized method of information sharing between parties that do not trust each other.

Instead the trust is in the underlying cryptographic algorithms. This practical introductory course provides hand-on experience with the fundamentals of cryptography codes and ciphers, symmetric and asymmetric encryption, public and private keys, hashes, and zero knowledge proofs — as it is applied to implementing a blockchain solution. This course covers the basics of a distributed ledger, how it is built, used, and secured at the network and data-structure levels. Methods of ensuring consensus — from proof-of-work to more complex solutions e. Students will have both written and practical, Python-based, assignments to build and deploy components of a blockchain solution.

You know how to program, but do you know how computers really work? How do millions of transistors come together to form a complete computing system? This bottom-up course begins with transistors and simple computer hardware structures, continues with low-level programming using primitive machine instructions, and finishes with an introduction to the C programming language.

The purpose of this course is to provide a 1 CU educational experience which tightly integrates the theory and applications of discrete probability, discrete stochastic processes, and discrete statistical inference in the study of computer science. The intended audience for this class is both those students who are CS majors as well as those intending to be CS majors. This course could be taken immediately following CIS Computation and Programming will play an essential role in this course.

The students will be expected to use the Maple programming environment in homework exercises which will include: numerical and symbolic computations, simulations, and graphical displays.

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This course explores questions fundamental to computer science such as which problems cannot be solved by computers, can we formalize computing as a mathematical concept without relying upon the specifics of programming languages and computing platforms, and which problems can be solved efficiently. The topics include finite automata and regular languages, context-free grammars and pushdown automata, Turing machines and undecidability, tractability and NP-completeness.

The course emphasizes rigorous mathematical reasoning as well as connections to practical computing problems such as text processing, parsing, XML query languages, and program verification. How do you optimally encode a text file? How do you find shortest paths in a map? How do you design a communication network? How do you route data in a network? What are the limits of efficient computation? This course gives a comprehensive introduction to design and analysis of algorithms, and answers along the way to these and many other interesting computational questions.

You will learn about problem-solving; advanced data structures such as universal hashing and red-black trees; advanced design and analysis techniques such as dynamic programming and amortized analysis; graph algorithms such as minimum spanning trees and network flows; NP-completeness theory; and approximation algorithms. This course introduces principles and practices of computer and network security.


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We will cover basic concepts, threat models, and the security mindset; an introduction to cryptography and cryptographic protocols including encryption, authentication, message authentication codes, hash functions, public-key cryptography, and secure channels; an introduction to networks and network security including IP, TCP, routing, network protocols, web architecture, attacks, firewalls, and intrusion detection systems; an introduction to software security including defensive programming, memory protection, buffer overflows, and malware; and discuss broader issues and case studies such as privacy, security and the law, digital rights management, denial of service, and ethics.

Can you check if two large documents are identical by examining a small number of bits?

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Can you verify that a program has correctly computed a function without ever computing the function? Can students compute the average score on an exam without ever revealing their scores to each other? Can you be convinced of the correctness of an assertion without ever seeing the proof? The answer to all these questions is in the affirmative provided we allow the use of randomization.

Course Information

Over the past few decades, randomization has emerged as a powerful resource in algorithm design. This course would focus on powerful general techniques for designing randomized algorithms as well as specific representative applications in various domains, including approximation algorithms, cryptography and number theory, data structure design, online algorithms, and parallel and distributed computation. Semesters Offered: TBA. You know how to program, but do you know how to implement a programming language?

Topics covered include: lexical analysis, grammars and parsing, intermediate representations, syntax-directed translation, code generation, type checking, simple dataflow and control-flow analyses, and optimizations. Along the way, we study objects and inheritance, first-class functions closures , data representation and runtime-support issues such as garbage collection.

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This is a challenging, implementation-oriented course in which students build a full compiler from a simple, typed object-oriented language to fully operational x86 assembly. The course projects are implemented using OCaml, but no knowledge of OCaml is assumed. This course introduces students to various tools source control, automated build systems, programming environments, test automation, etc. Topics will include: software development lifecycle; agile and test-driven development; source control and continuous integration; requirements analysis; object-oriented design and testability; Android application development; software testing; refactoring; and software quality metrics.

This is the second computer organization course and focuses on computer hardware design. Basic cache coherence and synchronization. Offered: Spring Course Website.

Do We Need a New Way to Teach Math?

This course surveys methods and algorithms used in modern operating systems. Concurrent distributed operation is emphasized. This introductory course will present basic principles of robotics with an emphasis to computer science aspects. Algorithms for planning and perception will be studied and implemented on actual robots.

While planning is a fundamental problem in artificial intelligence and decision making, robot planning refers to finding a path from A to B in the presence of obstacles and by complying with the kinematic constraints of the robot. In this course, algorithms will be implemented in Python on mobile platforms on ground and in the air.

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No prior experience with Python is needed but we require knowledge of data structures, linear algebra, and basic probability. The purpose of this course is to introduce undergraduate students in computer science and engineering to quantum computers QC and quantum information science QIS. This course is meant primarily for juniors and seniors in CIS. No prior knowledge of quantum mechanics QM is assumed. Design and implementation of a significant piece of work: software, hardware or theory.

In addition, emphasis on technical writing and oral communication skills.