Module Database Search
| MODULE DESCRIPTOR | |||
|---|---|---|---|
| Module Title | |||
| Advanced Artificial Intelligence | |||
| Reference | CM4107 | Version | 5 |
| Created | September 2023 | SCQF Level | SCQF 10 |
| Approved | August 2017 | SCQF Points | 15 |
| Amended | April 2024 | ECTS Points | 7.5 |
| Aims of Module | |||
|---|---|---|---|
| To improve understanding of modern artificial intelligence (AI) by learning to code, debug and train machine learning algorithms. Students will learn about the theory as well as the implementation of state-of-the-art supervised and unsupervised algorithms. The module will use popular examples to showcase AI applications in reasoning and decision-making, language understanding, image and activity recognition tasks. | |||
| Learning Outcomes for Module | |
|---|---|
| On completion of this module, students are expected to be able to: | |
| 1 | Question the principal theories, concepts and methods used in the development of intelligent systems. |
| 2 | Conceptualise complex machine learning algorithms. |
| 3 | Operate a wide range of AI models for real-world problems. |
| 4 | Argue the importance of data quality and awareness of ethics and transparency issues related to AI. |
| 5 | Critique relevant literature in AI. |
| Indicative Module Content |
|---|
| Fundamentals of logic, reasoning and machine learning. Supervised and unsupervised machine learning including neural nets, support vector machines, decision trees, probabilistic learning, instance-based learners, metric learning and clustering algorithms. Real-World Applications for instance in the areas of classification, Image analysis, Natural language understanding. |
| Module Delivery |
|---|
| Key concepts are introduced and illustrated through lectures and directed reading. The understanding of students is tested and further enhanced through interactive tutorials. In the laboratories, the student will progress through a sequence of exercises to develop sufficient knowledge and skills in Artificial Intelligence. |
| Indicative Student Workload | Full Time | Part Time |
|---|---|---|
| Contact Hours | 30 | N/A |
| Non-Contact Hours | 120 | N/A |
| Placement/Work-Based Learning Experience [Notional] Hours | N/A | N/A |
| TOTAL | 150 | N/A |
| Actual Placement hours for professional, statutory or regulatory body |
| ASSESSMENT PLAN | |||||
|---|---|---|---|---|---|
| If a major/minor model is used and box is ticked, % weightings below are indicative only. | |||||
| Component 1 | |||||
| Type: | Coursework | Weighting: | 100% | Outcomes Assessed: | 1, 2, 3, 4, 5 |
| Description: | A coursework based on a case study and critical analysis. | ||||
| MODULE PERFORMANCE DESCRIPTOR | |
|---|---|
| Explanatory Text | |
| The student must achieve a D in C1. | |
| Module Grade | Minimum Requirements to achieve Module Grade: |
| A | The student must achieve an A in C1. |
| B | The student must achieve a B in C1. |
| C | The student must achieve a C in C1. |
| D | The student must achieve a D in C1. |
| E | The student must achieve an E in C1. |
| F | The student must achieve an F in C1. |
| NS | Non-submission of work by published deadline or non-attendance for examination |
| Module Requirements | |
|---|---|
| Prerequisites for Module | CM3038 Artificial Intelligence For Problem Solving or equivalent. |
| Corequisites for module | None. |
| Precluded Modules | None. |
| INDICATIVE BIBLIOGRAPHY | |
|---|---|
| 1 | Russell and Norvig. Artificial Intelligence: A Modern Approach. |
| 2 | Raschka. Python Machine Learning. Packt |
| 3 | N D LEWIS, 2016, Deep Learning Step by Step with Python |
| 4 | RASHID T, 2016, Make Your Own Neural Network, CreateSpace Publishing |
| 5 | BISHOP C, 2006 , Pattern Recognition and Machine Learning, Springer |
| 6 | KOWALSKI R, 2011, Computational Logic and Human Thinking, Cambridge University Press. |
| 7 | ERTEL W, 2011, Introduction to Artificial Intelligence, Springer |