CSC401 Fundamentals of Computer Science Assignment Sample UITM Malaysia

CSC401- Fundamentals of Computer Science Assignment Sample UITM Malaysia

This course will introduce you to the basics of computer science. You’ll learn about how a computer system is put together, what all its various parts do and why they are important for operation, as well as ideas on using circuitry theory principles in solving data transfer problems or control issues.

CSC401- Fundamentals of Computer Science UITM Malaysia is a course that introduces the basics of computer science. Topics covered include algebra, logic, and problem-solving. In this course, students will learn how to program in Java and C++ as well as explore how computers work on an architectural level. Students will create programs with loops, functions, arrays, and other data structures in order to solve problems in different areas such as mathematics or biology. This assignment sample provides a detailed overview for those who are considering taking CSC401- Fundamentals of Computer Science assignment help.

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Assignment Brief Of CSC401 Fundamentals of Computer Science UITM Malaysia

In this course, there are many types of assignments given to students like
a group project, individual assignment, report, business plan, business proposal, executive summary, and the solutions are given by us.
Upon completion of this assignment sample, students should be able to:

Assignment Activity 1: Apply the suitable theories and knowledge related to the fundamentals of computer science

This assignment example explains theories and knowledge related to the fundamentals of computer science. computer science is the scientific and practical approach to computation and its applications. It deals with the theoretical foundations of information processing by computer systems, as well as the design of systems and programming languages that allow people to write computer programs for carrying out specific tasks.

There are so many different branches in Computer Science it’s nearly impossible to list them all, but a few examples are Artificial Intelligence, Machine Learning, Computational Linguistics, HCI (human-computer interaction), Graphics VLSI (very large scale integration) Design, Computer Graphics Online gaming design, and Databases. It is also about understanding how current technology works as well as seeing how new technologies work alongside those we have today.

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Assignment Activity 2: Display the practical skills related to the fundamentals of computer science

In this assignment sample, students will get to know the practical skills related to the fundamentals of computer science there are two ways of identifying practical skills.

  1.  Hardware and Software Practical Knowledge: This type of knowledge is usually obtained through formal training and on-the-job training where users are exposed to different hardware devices or software programs. This information is not essential but would be an advantage for a computer science professional since these skills can only be applicable if you work in the field.
  2. Fundamental Skills: These are all the basic principles of computer science and practical decision-making skills that help create an organized workflow.
  3. Programming languages: Java, C++, Basic.
  4. Operating Systems: Windows/Linux Terminal.
  5. Programming Frameworks: Cocoa Framework for Mac OSX and Objective-C Framework for iOS.
  6. Programming IDE’s: MATLAB and Apple Xcode.

Assignment Activity 3: Illustrate scientific solutions related to fundamentals of computer science

This assignment example shows scientific solutions related to the fundamentals of computer science, there are many scientific solutions that directly relate to fundamental concepts of computer science, but oftentimes these can be overlooked by those not deeply involved in the field. Neural networks – which is research heavily used in the fields of machine learning and big data analysis – use an algorithmic pattern called a multilayer perceptron (MLP) as its core.

This MLP has linear layers with hyperbolic tangent activation functions that produce non-sigmoidal transfer functions; it also restricts network activations to the inside of the unit circle to avoid overstretched boundary conditions. These structurally simple features have been shown to produce deep learning results comparable or better than their predecessors while being significantly more computationally tractable and scalable.

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