Module Database Search
| MODULE DESCRIPTOR | |||
|---|---|---|---|
| Module Title | |||
| Machine Learning And AI | |||
| Reference | CM4131 | Version | 1 |
| Created | September 2023 | SCQF Level | SCQF 10 |
| Approved | January 2024 | SCQF Points | 15 |
| Amended | ECTS Points | 7.5 | |
| Aims of Module | |||
|---|---|---|---|
| To provide students with the ability to demonstrate the knowledge and practical skills required for the development of intelligent systems, including the application of machine learning, in solving real-world problems. | |||
| Learning Outcomes for Module | |
|---|---|
| On completion of this module, students are expected to be able to: | |
| 1 | Examine the use of machine learning and artificial intelligence techniques in real-world applications. |
| 2 | Justify the strengths and limitations of current machine learning and artificial intelligence techniques for a given problem. |
| 3 | Discuss the evaluation of a model learnt from data. |
| 4 | Develop an intelligent system using suitable machine learning and/or artificial intelligence techniques to solve a given problem. |
| Indicative Module Content |
|---|
| Artificial Intelligence - definition, concepts, and examples; Intelligent behaviour; Case-based reasoning; Genetic Algorithms; Supervised and unsupervised machine learning including neural nets, support vector machines, decision trees, probabilistic learning, instance-based learners, metric learning and clustering algorithms. Convolutional Neural Networks and Deep Learning. Real-World Applications. Ethical AI. |
| Module Delivery |
|---|
| The course is lecture and laboratory based. The lectures introduce key concepts to give students an awareness of the relevant issues in the development of intelligent systems. The laboratories will allow the student to progress through a sequence of exercises to develop practical skills in the application of AI and machine learning. The understanding of the student is further enhanced through directed reading. |
| 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: | Practical Exam | Weighting: | 100% | Outcomes Assessed: | 1, 2, 3, 4 |
| Description: | |||||
| MODULE PERFORMANCE DESCRIPTOR | |
|---|---|
| Explanatory Text | |
| For a pass, 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 an B in C1. |
| C | The student must achieve an C in C1. |
| D | The student must achieve an 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 | None. |
| Corequisites for module | None. |
| Precluded Modules | None. |
| INDICATIVE BIBLIOGRAPHY | |
|---|---|
| 1 | RUSSELL, S. and NORVIG, P. 2021. Artificial Intelligence: A Modern Approach. 4th ed. Pearson. ISBN-13: 9781292401133 (hard copy), ISBN-13: 9781292409399 (eBook) |
| 2 | BURKOV, A. 2019, The Hundred-Page Machine Learning Book, Andryi Burkov, ISBN-13 978-1777005474 |
| 3 | AGGARWAL, C. 2023, Neural Networks and Deep Learning: A Textbook. 2nd ed., Springer, ISBN-13 978-3031296413 |
| 4 | FINLAY, S., 2021. Artificial Intelligence and Machine Learning for Business: A No-Nonsense Guide to Dat, Driven Technologies. Realtivistic, 4th ed. ISBN-13 978-1999325381 |
| 5 | GERON, A. 2019, Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems , O’Reilly, ISBN-13 978-1492032649 |