| Course code | AIST1110 |
| Course title | Introduction to Computing using Python 計算導論 (Python) |
| Course description | This course aims to provide an intensive hands-on introduction to the Python programming language. Topics include Python programming language syntax, basic data types, operators for various data types, function definition and usage, file and operating system support, object-oriented programming, functional programming, module creation, visualization, multi-threaded programming, networking, cryptography, web/database access. The course will go through some important Python packages for artificial intelligence and machine learning applications, e.g., NumPy and SciPy, and use these packages to accomplish some simple artificial intelligence and machine learning tasks. 本科旨在為Python編程語言提供密集的實踐介紹。主題包括基本的Python語言語法、基本數據類型、各種數據類型的基本運算符、函數定義和使用、文件和操作系統支援、物件導向編程、函數式編程、模塊創建、可視化、多線程編程、網絡、密碼學、網絡/數據庫存取。本科還將簡介重要的Python軟件包，用於人工智能和機器學習應用，如Numpy和Scipy及使用這些軟件包來完成一些簡單的人工智能及機器學習任務。 |
| Unit(s) | 3 |
| Course level | Undergraduate |
| Semester | 1 |
| Pre-requisites | ENGG1110 or ESTR1002 |
| Exclusion |
CSCI1020 or 1030 or 1040 or 1110 or 1120 or 1130 or 1510 or 1520 or 1530 or 1540 or 2040 or ESTR1100 or 1102 |
| 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 outcomes | At the end of the course of studies, students will have acquired the ability to 1. write, compile and execute Python programs; 2. make use of Python’s object-oriented programming methodology; 3. make use of Python’s functional programming methodology; 4. design and create applications using Python modules; 5. include other programming languages (e.g., C programming language) into Python; 6. use Python for database and web access; 7. use Python for 2D and 3D visualization. |
| Assessment (for reference only) |
Essay test or exam: 60% Lab reports: 40% |
| Recommended Reading List | 1. Exploring Python by Timothy A. Budd 2. Think Python: How to Think Like a Computer Scientist, by Allen B. Downey 3. Python for Informatics: Exploring Information, by Chuck Severance 4. Artificial Intelligence: Foundations of Computational Agents, by David Poole, Alan Mackworth |
| AISTN programme learning outcomes | Course mapping |
| Upon completion of their studies, students will be able to: | |
| 1. apply knowledge of mathematics, science, and engineering appropriate to the AI degree discipline (K/S); | Y |
| 2. design and conduct experiments, as well as to analyze and interpret massive data (K/S); |
Y |
| 3. 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); | |
| 4. identify, formulate and solve AI-related engineering problems (K/S); |
Y |
| 5. understand professional and ethical responsibility (K/V); |
|
| 6. communicate and work effectively in multi-disciplinary teams (S/V); | |
| 7. 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); | |
| 8. recognize the need for and the importance of life-long learning (V); and | |
| 9. use the techniques, skills, and modern computing tools necessary for engineering practice appropriate to the AI and computing discipline (K/S). |
Y |
| Remarks: K = Knowledge outcomes; S = Skills outcomes; V = Values and attitude outcomes | |