Introduction to Artificial Intelligence Assignment Brief
| Qualification | OTHM Level 7 Diploma in Artificial Intelligence (610/4802/1) |
| Unit Reference Code | T/651/3604 |
| Unit Name | Introduction to Artificial Intelligence |
| Credit | 20 |
| GLH | 100 |
| TQT | 200 |
| Mandatory / Optional | Mandatory |
| Unit Grading Type | Pass / Fail |
Assignment Aim
This unit aims to provide learners with a comprehensive introduction to the field of Artificial Intelligence (AI), covering both classical and modern approaches. Learners will explore the fundamental concepts, techniques, and philosophies underlying AI, including knowledge representation, reasoning, machine learning (including an overview of neural networks, the biological basis of neural networks as models of neurons in the brain, and non-linear activations analogous to spiking), and search algorithms. The unit also examines the ethical and philosophical implications of AI, as well as its future challenges. By completing this unit, learners will gain the foundational knowledge necessary to engage with more specialized AI topics in advanced studies.
Learning Outcomes and Assessment Criteria
| Learning Outcome – The learner will: | Assessment Criteria – The learner can: |
| 1. Understand the fundamental concepts and approaches in AI. | 1.1 Describe the key classical and modern approaches to AI. 1.2 Explain the significance of modern benchmarks for AI beyond the Turing test. 1.3 Explain the limitations of the Church-Turing thesis in modern AI development. 1.4 Analyse the philosophical debates surrounding AI, including the Turing test and Searle’s Chinese Room argument. 1.5 Evaluate the principal achievements and shortcomings of AI. 1.6 Assess the future challenges and ethical considerations of AI development. |
| 2. Be able to apply search algorithms in AI problem-solving. | 2.1 Describe different types of search algorithms used in AI. 2.2 Explain the differences between finding satisfactory paths and optimal paths. 2.3 Critically analyse the effectiveness of heuristic search methods in problem-solving. 2.4 Evaluate the application of search algorithms in real-world AI problems. 2.5 Develop a simple AI program utilizing search algorithms to solve a given problem. |
| 3. Understand the principles of knowledge representation and reasoning in AI. | 3.1 Describe various methods of knowledge representation used in AI. 3.2 Explain the concepts of monotonic and non-monotonic reasoning. 3.3 Analyse the role of data-driven and goal-driven reasoning in AI. 3.4 Evaluate the challenges of reasoning under uncertainty in AI. 3.5 Develop a reasoning system using knowledge representation techniques. |
| 4. Be able to apply machine learning techniques in AI. | 4.1 Describe and compare machine learning techniques, including Logistic Regression and Kernel Methods. 4.2 Explain the process of inductive and deductive learning in AI. 4.3 Analyse the role of classification and regression trees in machine learning. 4.4 Critically evaluate the effectiveness of Perceptrons and introduce Support Vector Machines (SVMs). 4.5 Develop a machine learning model to solve a specific problem. |
| 5. Understand the ethical and societal implications of AI. | 5.1 Describe the key ethical concerns associated with AI development and deployment. 5.2 Explain the importance of responsible AI development and governance. 5.3 Critically analyse the potential societal impacts of widespread AI adoption. 5.4 Evaluate the role of international collaboration in addressing global AI challenges. 5.5 Develop recommendations for ensuring ethical AI practices in a given context. |
Assessment
To achieve a ‘pass’ for this unit, learners must provide evidence to demonstrate that they have fulfilled all the learning outcomes and meet the standards specified by all assessment criteria.
| Learning Outcomes to be met | Assessment Criteria to be covered | Assessment type | Word count (approx. length) |
| LO1-LO5 | All AC’s under LO1-LO5 | Coursework | 4500 words |
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