CSC583 Artificial Intelligence Algorithms Assignment Example Malaysia
The aim of this course is to introduce students not only to the fundamentals of key intelligent systems technologies but also how these can be integrated for science and engineering applications. Topics covered include Expert Systems; Neural Networks (including supervised and unsupervised learning); Fuzzy Logic Technology – including crisp/uncrisp reasoning as well as degrees-of-freedom analysis employing fuzzy sets theory or Collins’ qualitative measure theorem among others things; Rule-Based Systems – covering both production-systems as well as Petri Nets; Genetic Algorithms and Swarm Intelligence.
This course will also cover the Mathematics of intelligent systems technology, including techniques from probability theory, statistics, algebra, and calculus. The aim is to provide an introduction to reasoning using uncertainty (probability) by providing the necessary mathematical tools.
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There will be an introduction to expert systems, including knowledge acquisition techniques and their implementation using common knowledge representation languages – e.g. production rules, frames, neural networks. Basic AI issues such as search methods, heuristics and logic formalisms are also introduced.
We then move on to fuzzy logic theory, providing the mathematical underpinnings for reasoning with approximate truth representation, followed by two applications of fuzzy sets – linguistic variables and diagnosis. The concepts are illustrated using simple examples before moving onto complex engineering problems involving multiple parameters. Next, there is an overview of how fuzzy systems can be implemented in practice along with some case studies to illustrate the application of AI systems in industry (such as robotics, control, and aerospace).
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In this course, there are many types of assignments given to students like a group project, individual assignment, and the solutions are provided by us. Upon completion of this course you should be able to:
Assignment Task 1: Apply concepts of artificial intelligence
Artificial intelligence has many different definitions depending on the perspective. What is meant by artificial intelligence (AI) is not one generalization, but rather can be separated into sub fields of AI called “weak” or “strong.” For example, machine learning would fall under the category of strong AI, but computer chess falls under weak AI. Weak AI is software that only does something somebody else programmed it to do, whereas strong AI leverages machine learning to do tasks with variability. However no matter what definition you use for AI there are two things that remain true- all software needs input and nothing comes without a cost. One way that costs manifest themselves are in time constraints because programs take time to calculate actions based on parameters set by people.
However, there are some concepts from AI that can be applied to trading. For example, the most common usage of AI in finance is algorithmic trading which uses computer programs to generate buy and sell signals based on certain criteria. These signals are then used by traders or investors to automate their strategies. This concept can be applied in two ways: using machine learning algorithms to automate certain tasks for day traders or using an algorithm to generate buy and sell signals for manual traders. Another example of a concept from AI that can be applied to trading is diversification. In machine learning terms, diversification is called “bagging.” Bagging uses multiple models trained on different samples drawn randomly with replacement from the same population in order to improve accuracy by reducing variance or noise in the estimates.
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Assignment Task 2: Construct problem-solving in artificial intelligence methods
In artificial intelligence, problem-solving programming, decision making, and logical reasoning are all closely related topics. There is also an overlap between machine learning and computer parsable data formats.
Various methods and models can be used to solve a given problem depending on the situation. For example, by using heuristic search algorithms we can efficiently map out the space of all possible solutions for a given problem. To provide greater insight into what could be done with such heuristics we will focus throughout this article on each of these different strategies individually because there’s no “one size fits all.”
The research about how these problems are solved within artificial intelligence has advanced its techniques over decades as well as inspired new ideas that have been successful in artificial intelligence.
This includes the ability to make logical decisions, take appropriate actions or behave according to a given set of rules.
Since it is difficult to reason about the specifics of any domain, many programs will instead use an inductive approach. They use specific examples to form general rules that can be used for making future decisions.
For example, a program may learn that whenever it’s raining outside and the driveway is wet, this means that there has been recent rainfall. They may then generalize this specific case to create a rule that states “if the driveway is wet, it rained.”
Within artificial intelligence, there are many rules of thumb that provide solutions to common problems. Wherever possible these programs should be built using these simple algorithms or even libraries of reusable components.
Assignment Task 3: Demonstrate professionalism in artificial intelligence methods
This is a difficult one to answer in the way you want me to. Artificial intelligence methods can be described as any method that helps establish or improve artificial intelligence, including statistical and machine-learning techniques. This has some similarity with the concept of “methods,” which can be described as approaches for achieving an endpoint like a demonstrable skill or proficiency in some task or activity–only that’s more general than what we’re looking for here.
Expertise in AI methodology is largely dependent on either education and/or research experiences, mixed with expertise outside of such areas, such as knowledge of programming languages.
A lack of professionalism in artificial intelligence methods can have a range of consequences, from causing embarrassment for one’s client to ethical dilemmas. It is helpful to keep the following points in mind as one works with AI:
Some facts about professionalism you may not know:
- Professionalism is often characterized as living up to a set of ethics or conduct requirements that have been agreed upon by those involved. In the case of professionals, this generally means that those involved (employer and worker) establish an understanding and expectations about competency and behavior at work. This could be discussed or established through a contract or agreement between two parties. When these standards aren’t explicit, but customary expectations arise according to social norms, professionalism is considered good practice.
- In order to be a professional, one must display a commitment to excellence and ethical behavior in their work. This means that professionals are expected to adhere to the highest industry standards, both with regard to the proficiency of their skills, as well as conduct outside of work-related issues. There is an expectation that professionals are ethical in their dealings with other professionals, employers, work related associates, and the public.
- The standards for professional conduct are generally agreed upon by those involved. These are often based on ethics codes or oaths individuals take to uphold certain principles of performance within the profession. For example, medical doctors may have an ethical code they follow which includes expectations for honesty, competence, confidentiality, and other standards.
As a third-party service provider performing services through an API that provides suggestions for computer scientists on what to study next, I feel that professionalism can be demonstrated by providing accurate information based on reasonable assumptions. It is important to conduct due diligence when developing algorithms in order to ensure there are no biases included in the data used to make decisions.
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