1.
| Understand the basics and general frameworks of the common AI approaches (e.g., search, machine learning, deep learning) for problem solving |
2.
| Understand the workflow, the capabilities and the limitation of some state-of-the-art AI technologies, platforms and tools for solving data intensive problems with AI approaches |
3.
| Identify potential AI-oriented tasks in a problem and design corresponding AI-powered solutions |
4.
| Use AI tools and apply AI techniques and considerations properly in solving data intensive problems |
Mapping from Course Learning Outcomes to Programme Learning Outcomes
| PLO a | PLO b | PLO c | PLO d | PLO e | PLO f | PLO g | PLO h | PLO i | PLO j |
CLO 1 | T | T | | | | | | | | |
CLO 2 | T | T | | | | | | | | T |
CLO 3 | | | T | T | | | | | | |
CLO 4 | | | | | T | T | T | T | T | T |
T - Teach, P - Practice
For BEng(CompSc) Programme Learning Outcomes, please refer to
here.
|
Syllabus |
Calendar Entry:
This course focuses on practical applications of AI technologies. The course comprises two main components: students first acquire the knowledge and know-how of the state-of-the-art AI technologies, platforms and tools (e.g., TensorFlow, PyTorch, Open AI, scikit-learn, Azure AI) via self-learning of designated materials including open courseware. Students will then explore practical AI applications and complete a course project which implements an AI-powered solution to a problem of their own choice.
|
Detailed Description:
Self-Learning |
Mapped to CLOs
|
Introduction to AI methodologies | 1, 2 |
Selected state-of-the-art AI platforms & tools | 1, 2 |
Project |
Mapped to CLOs
|
Problem Identification | 1, 2, 3 |
Implementation | 1, 2, 3, 4 |
Demonstration & Presentation | 1, 2, 3, 4 |
|
Assessment:
Continuous Assessment:
70% Written Examination:
30%
|
Teaching Plan |
Please refer to the corresponding Moodle course.
|
Moodle Course(s) |
|