CSC415 Fundamentals Of Computer Problem Solving Assignment Example UITM Malaysia
CSC415 Fundamentals Of Computer Problem Solving is a course UiTM offers that explores the fundamental concepts of algorithms and data structures. The course is designed to provide students with a strong foundation in problem-solving, which will be invaluable in their future studies and careers.
The topics covered in this course include basic algorithmic techniques, graph algorithms, various data structures such as trees, heaps, and hash tables, and algorithmic analysis. Throughout the lectures and assignments, students will have opportunities to practice their problem-solving skills through real-world examples drawn from different application domains.
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In this section, we are providing some assignment activities. These are:
Assignment Activity 1: Demonstrate An Understanding Of The Fundamentals Of Computer Problem Solving.
To effectively solve computer problems, one must have a strong understanding of the fundamentals of computer science. This includes knowledge in areas such as algorithms, data structures, software engineering, and more.
Additionally, to troubleshoot problems, it is essential to be familiar with the inner workings of computers and operating systems. This could include knowing areas such as system architecture, low-level programming, and network protocols.
By having a strong foundation in both computer science and system administration/troubleshooting, one can become an effective problem solver when it comes to computers.
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Assignment Activity 2: Construct Algorithmic Solution According To A Given Problem.
When it comes to constructing an algorithmic solution, there are a few key steps that need to be taken to produce a successful outcome. Problems can be solved in a variety of ways, but an effective algorithm will always take the following into account:
The first step is to understand the problem. This seems like a no-brainer, but it’s the most important part of solving any problem, whether with an algorithm or not. Without understanding what you’re trying to accomplish, it’s impossible to find the right solution. So take some time to think about what the problem is asking for, and what restrictions have been set in place.
Once you have a good understanding of the problem, the next step is to come up with a plan of attack. This is where you’ll start to think about what steps need to be taken to solve the problem. It’s important to think about the inputs and outputs of each step, as well as how each step will lead to the next.
After you have a plan, it’s time to start coding. This is where you’ll take the steps you’ve laid out and turn them into actual code that can be executed by a computer. It’s important to test your code as you go, to make sure that each step is working as expected.
Finally, once your code is complete, it’s time to analyze the results. This is where you’ll take a look at the output of your algorithm and see if it produces the desired results. If not, it’s back to the drawing board to see what went wrong and how it can be improved.
Assignment Activity 3: Perform Autonomous Learning Related To Computer Problem-Solving Task.
There are several ways to perform autonomous learning related to computer problem-solving tasks. One way is to use a neural network. A neural network is composed of a large number of interconnected processing nodes, and these nodes can be configured to learn how to solve problems on their own through a process called training.
Another approach is to use genetic algorithms. Genetic algorithms are inspired by the process of natural evolution, and they work by generating a large number of potential solutions to a problem, then testing and ranking these solutions based on how well they solve the problem. Solutions that perform better are then used as parents to generate new solutions, which are in turn tested and ranked. This process is repeated until the best solution is found.
Finally, another approach that can be used for autonomous learning is reinforcement learning. Reinforcement learning is a type of learning where an agent is rewarded for taking actions that lead to the successful completion of a task. The agent then uses this feedback to modify its behaviour to increase the chances of success in the future. This process is repeated until the agent has learned how to solve the problem optimally.
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