YEAR 1 SEM 2
(AY 20/21 Jan – May 2021)
Courses Taken:
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CZ1115: Introduction to Data Science & Artificial Intelligence
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ML0003: Kickstart Your Career Succes
CZ1104: Linear Algebra for Computing
Lecturer: Asst Prof Lana Obraztsova, Dr Tay Kian Boon
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Assessment & Grading:
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2 * Quizzes (40%)
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4 * Lab (Code + Online Quizzes) (20%)
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Final Examination (40%)
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CZ1104 is a new course aiming to support us to learn mathematical concepts related to linear algebra. There is some overlap in the content of MH1200 (i.e. Part1: System of Linear Equation, Matrix Algebra, Determinants and Vector Space). CZ1104 focuses more on application, while MH1200 pays more attention to logical understanding and connection between each concept. As for Part 2, it includes Orthogonality, Gram-Schmidt Process, QR Factorisation, Least Squares Approximation, Eigen and Singular Values. To be honest, I feel neutral about their teaching. Especially the first lecturer, Prof Lana, her teaching pace is soooo slow and I don’t actually learn anything new.
The labs test on python skills using NumPy library. All the exams are quite easy and focus on computation of answers. In summary, the module is relatively manageable, and should be extremely simple for anyone who has taken MH1200. CZ1104 lays a solid foundation for continuous in-depth study on Computer Science, specifically, Machine Learning, Computer Vision etc.
CZ1106: Computer Organisation & Architecture
Lecturer: Assoc Prof Goh Wooi Boon, Asst Prof Mohamed M. Sabry, Mr Oh Hong Lye
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Assessment & Grading:
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4 * Lab Quizzes (40%)
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Mid Semester E-test (30%)
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Final Examination (30%)
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This module teaches students the way that hardware components are designed and connected and how it determines the size and capabilities of computers. The first part focuses more on how High-level Software actually stores and runs to Low-level Instructions. The second half is very content heavy and memory intensive. Because of that, it messed up my passion on it. The module should have been interesting in my impression.
Expect to put in quite a bit of effort on this module (relative to other modules). I should have taken tutorials and labs seriously, gaining a deeper understanding of knowledge. As for the final exam, the earlier you start doing past year paper, the better. This paper has very similar type of question. After completing and summarizing all the pyp, you will be well-equipped to face exam questions.​​​​​
CZ1107: Data Structures & Algorithms
Lecturer: Dr Owen Noel Newton Fernando, Dr Loke Yuan Ren
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Assessment & Grading:
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8 * Assignments (40%)
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2 * Lab Tests (40%)
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Final Quiz (20%)
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CZ1107 is one of the most important modules although this is in the introductory level. The module starts by introducing elementary data structure, Linked List, Stack, Queue and Tree. Algorithm Topics covered include Analysis of Algorithm (Time and Space Complexity), Searching, Graph and Combinatorial problems, for example Backtracking, Dynamic Programming, Matching Problem. large portion of the grade comes from weekly assignments that require students to write code based on concepts taught. The difficulty level of assignments is higher than the lab tests, but there are plenty of time to think and work them out. You can also discuss with your friends.
I also have to remind you that submitting other people's work in any attempt is considered as cheating. You also should not simply share your work to anyone. If you choose to do so, you have to bear the consequence later (That was why I got just 1.90/5 of my assignment 5).
CZ1115: Introduction to Data Science and Artificial Intelligence
Lecturer: Dr Sourav Sen Gupta, Assoc Prof Bo An
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Assessment & Grading:
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Quizzes within LAMS Sequences (10%): 600.0/600.0
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2 * Theory Quizzes based on Lectures (40%): DS: 27.0/30.0, AI: 50.0/50.0
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Lab Exercises (4) and Quiz (20%): Exercises: 39.0/40.0, DS Programming Quiz: 30.0/30.0
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Mini-Project (Group) (30%)
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Workload for this module seems to be relatively light, apart from the project. However, the project was released quite early into the semester, so students had the flexibility to plan their time appropriately.
EE1003: Introduction to Materials for Electronics
Lecturer: Assoc Prof Kantisara Pita, Assoc Prof Wang Hong
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Assessment & Grading:
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2 * Quizzes (10 MCQ) (20%)
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11 * Weekly OASIS Homework (20%)
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Final Exam (60%)
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The Weekly OASIS Homework was manageable. So really try to aim for full marks if possible. But it should be noted that be serious and careful with them, even the simplest one.
EG0001: Engineers & Society
Lecturer: Dr Lum Kit Meng
Tutor: Dr Quah Tong Seng
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Assessment & Grading:
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Online Quiz (30%)
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Oral presentation (70%)
This is the hell module!!!! I really don’t enjoy this module at all!!! The only essence of existing the module is to pull my gpa down. I don’t think I learnt anything in this module.
I made up for all recorded videos in one week by going through the ppt instead of watching the recorded videos. And then did a lot of the pyp. I will give myself 7/10 points to my final exam performance. But to be honest, if give me another chance for this module, I won’t still follow the weekly lectures.
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Be thankful and stop comlaining!!!!!!!! Needless to say, I must work hard at English!!
Online Courses
ML0003: Kickstart Your Career Succes
MOOC2: The Science of Well-being
Science of Well-being is a course designed to increase learners’ happiness and build more productive habits, which sounds very attractive and useful. It provides a fun way of learning about personal thought processing, attitudes, and well-being. I have learnt some misconceptions about happiness, annoying features of the mind that lead us to think the way we do, and the research that can help us change. Overall, it is a very helpful course to get me to assess my like and how to make the changes I need to improve my well-being.
