| Course code | AIST1000 |
| Course title | Introduction to Artificial Intelligence and Machine Learning 人工智能與機器學習入門 |
| Course description | This course covers the basic concepts, problems, approaches and applications of artificial intelligence and machine learning. It provides an introduction to various topics in AI systems and technologies, e.g., an overview of AI, machine learning theory and methods, ML in data science, neural networks and deep learning, hardware and software technologies for AI systems, natural language processing, computer vision, AI in games and sports, biomedical intelligence, intelligent manufacturing and robotics, ethical and legal issues with AI, etc. It discusses the applications of engineering principles to selected AI and ML problems. It also explores the future possibilities and challenges of AI. 本科涵蓋人工智能和機器學習的基本概念、問題、方法和應用，介紹AI系統和技術的各個主題，例如：AI概述、機器學習理論和方法、數據科學、神經網絡和深度學習、AI系統的硬件和軟件技術、自然語言處理、計算機視覺、遊戲和運動中的AI、生物醫學智能、智能製造和機器人技術、有關AI的道德和法律問題等。本科討論工程原理在某些AI和ML問題中的應用，亦探討AI在未來的可能性和挑戰。 |
| Unit(s) | 1 |
| Course level | Undergraduate |
| Semester | 1 |
| Grading basis | Graded |
| Grade Descriptors | A/A-: EXCELLENT – exceptionally good performance and far exceeding expectation in all or most of the course learning outcomes; demonstration of superior understanding of the subject matter, the ability to analyze problems and apply extensive knowledge, and skillful use of concepts and materials to derive proper solutions. B+/B/B-: GOOD – good performance in all course learning outcomes and exceeding expectation in some of them; demonstration of good understanding of the subject matter and the ability to use proper concepts and materials to solve most of the problems encountered. C+/C/C-: FAIR – adequate performance and meeting expectation in all course learning outcomes; demonstration of adequate understanding of the subject matter and the ability to solve simple problems. D+/D: MARGINAL – performance barely meets the expectation in the essential course learning outcomes; demonstration of partial understanding of the subject matter and the ability to solve simple problems. F: FAILURE – performance does not meet the expectation in the essential course learning outcomes; demonstration of serious deficiencies and the need to retake the course. |
| Learning objectives | At the end of the course of studies, students will have acquired 1. knowledge in basics and applications of artificial intelligence and machine learning; 2. an understanding of the limitations and possibilities of different approaches to artificial intelligence; 3. a general view of the overall curriculum of the AIST undergraduate programme, especially on the choice of streams. |
| Learning outcomes | At the end of the course of studies, students will 1. have some basic ideas of artificial intelligence and machine learning; 2. know the application areas of artificial intelligence and machine learning; 3. understand the limitations and possibilities of different approaches to artificial intelligence; 4. have a general picture of the overall curriculum of the AIST undergraduate programme, especially on the choice of streams. |
| Assessment (for reference only) |
Project ：50% Participation ：30% Attendance ：20% |
| Recommended Reading List | 1. Andrew Ng, “Machine Learning Yearning (Draft Version),” 2018 2. https://www.deeplearning.ai/machine-learning-yearning/ (assessed November 1, 2019) |
| AISTN programme learning outcomes | Course mapping |
| Upon completion of their studies, students will be able to: | |
| 1. identify, formulate and solve AI-related engineering problems (K/S); | Y |
| 2. design a system, component, or process to meet desired needs within realistic constraints, such as economic, environmental, social, political, ethical, health and safety, manufacturability and sustainability (K/S/V); |
Y |
| 3. understand the impact of AI solutions in a global and societal context, especially the importance of health, safety and environmental considerations to both workers and the general public (K/V); | |
| 4. communicate and work effectively in multi-disciplinary teams (S/V); |
|
| 5. apply knowledge of mathematics, science, and engineering appropriate to the AI degree discipline (K/S); |
|
| 6. design and conduct experiments, as well as to analyze and interpret massive data (K/S); | Y |
| 7. use the techniques, skills, and modern computing tools necessary for engineering practice appropriate to the AI and computing discipline (K/S); | |
| 8. understand professional and ethical responsibility (K/V); and | |
| 9. recognize the need for and the importance of life-long learning (V). |
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| Remarks: K = Knowledge outcomes; S = Skills outcomes; V = Values and attitude outcomes | |