Purpose
(The bried introduction about why should I learn this subject, what I should learn and what I can obtain after my study.)During undergraduate study in Software Engineering, There is no Artificial Intelligence major provided in UPM, and at that time, I didn't have a clear direction for my future academic path yet. Therefore, I missed the best opportunity to study artificial intelligence 4 years ago.
Unfortunately, the decision I made four years ago has affected my current choices. Due to my lack of background in artificial intelligence, I was unsuccessful in my graduate school applications. However, motivated by the willing to follow the trend of technological revolution and the fear of being replaced, I have no choice but to utilize online resources to study the necessary knowledge in Artificial Intelligence field independently.
The primary purpose of learning is to narrow the gap caused by my previous education and acquire a comprehensive understanding of Artificial Intelligence. By doing so, I aim to equip myself with the skills in this domain and to stay competitive in today's job market and satisfy personal interest of study.
The outcomes of this initiative are multifaceted. Firstly, I intend to learn basic knowledge in general AI techniques, which will enable me to apply them to solve real-world problems. Secondly, I aim to enhance my problem-solving abilities, strategies and thinking skills through practical projects and hands-on experience. Lastly, I will write some detailed learning logs that can serve as records for learning.
Learning Structure
The conclusion is that: to supplement the lack of knowledge in artificial intelligence since my undergraduate, the focus of my study will be on the following areas:
- Machine Learning
- Introduction to Data Science
- System Analysis and Design
- Natural Language Processing
- Deep Learning
- Evolutionary Computation
- Computer Vision and Pattern Recognition
- Practical Artificial Intelligence
Discussion
The specific course names and contents may vary from different universities. My bachelor study was completed from UPM in Malaysia. Therefore, to specified the case, the bachelor of artificial intelligence in University of Malaya's (UM), the best university in Malaysia, will be taken as reference for analysis. In fact, the undergraduate program in Artificial Intelligence is not significantly different from other types of computer science programs.
Most of the required courses and general education courses are essentially the same. By referring to UM undergraduate curriculum for Artificial Intelligence, the university public and faculty general courses are almost the same, such as Basic Malay Language, Computer Systems and Organization, Database and Data Structures. (There are mainly 4 categories about university courses: university core, faculty core, programme core and specialization elective)
The courses with gray-200 background are similar to the courses in B.S.E in UPM
| COURSE CODE | PROGRAMME CORE COURSES | CREDITS | TERM |
|---|---|---|---|
| WIA1002 | Data Structure (#WIX1002) | 5 | 2 |
| WIA1003 | Computer System Architecture (#WIX1003) | 3 | 2 |
| WIA1005 | Network Technology Foundation | 4 | 2 |
| WIA1006 | Machine Learning | 3 | 2 |
| WIA1007 | Introduction to Data Science | 3 | 1 |
| WIA2001 | Database | 3 | 1 |
| WIA2003 | Probability and Statistics | 3 | 1 |
| WIA2004 | Operating Systems | 4 | 2 |
| WIA2005 | Algorithm Design and Analysis (#WIA1002) | 4 | 2 |
| WIA2006 | System Analysis and Design | 3 | 1 |
| WIA2007 | Mobile Application Development | 4 | 1 |
| WIA3001 | Industrial Training * | 12 | 1 |
| WIA3002 | Academic Project I ** | 3 | 2 |
| WIA3003 | Academic Project II (#WIA3002) | 5 | 1 |
The differences lie in some specialized AI courses, such as Machine Learning, and certain elective courses, like Natural Language Processing, Computer Vision and Pattern Recognition, Practical Artificial Intelligence, and Deep Learning. In the AI program at UM, students need to choose 10 department electives out of a total of 14 offered, and not all of which are specifically related to AI.
| COURSE CODE | SPECIALIZATION ELECTIVE COURSES (Choose only 10 courses) | CREDITS | TERM |
|---|---|---|---|
| WIC2008 | Internet of Things | 3 | 2 |
| WID2001 | Knowledge Representation and Reasoning | 3 | 2 |
| WID2002 | Computing Mathematics II | 3 | 2 |
| WID2003 | Cognitive Science | 3 | 2 |
| WID3001 | Functional and Logic Programming | 3 | 2 |
| WID3002 | Natural Language Processing | 3 | 2 |
| WID3007 | Fuzzy Logic (#WIX1001) | 3 | 1 |
| WID3010 | Autonomous Robots | 3 | 2 |
| WID3011 | Deep Learning | 3 | 1 |
| WID3012 | Evolutionary Computation | 3 | 1 |
| WID3013 | Computer Vision and Pattern Recognition | 3 | 1 |
| WID3014 | Practical Artificial Intelligence | 3 | 1 |
| WID3015 | Numerical Analysis | 3 | 1 |
| WIG3004 | Virtual Reality | 3 | 2 |
Conclusion
From the above two tables, the following conclusions can be drawn: 1. The general education courses in both universities and colleges are 100% identical; 2. The similarity of the core specialized courses is an impressive 78%. In the SPECIALIZATION ELECTIVE courses, only 35% are directly related to AI. Even if students choose all AI courses within the SPECIALIZATION ELECTIVE, they can only complete 50% of the total credits required for the SPECIALIZATION ELECTIVE.
The courses that are directly about AI specialization are: Natural Language Processing, Deep Learning, Evolutionary Computation, Computer Vision and Pattern Recognition and Practical Artificial Intelligence. These courses focus specifically on topics and techniques related to artificial intelligence.