New: filter modules by day and time, student links, custom courses →
This course introduces the fundamental concepts of probability and statistics that underpin modern data science and machine learning. Students will learn both the foundations, such as probability theory, random variables, and statistical inference, and practical applications, including estimation, regression/classification models, Bayesian methods, and modern sampling techniques. By integrating probability and statistics with computational methods, the course aims to develop both rigorous understanding and practical problem-solving skills.
| AUs | 4.0 AUs |
| Grade Type | |
| Prerequisite | Must be a Turing AI Scholar MH1805 |
| Exam |
| Mon | Tue | Wed | Thu | Fri | |
|---|---|---|---|---|---|
| 930 | |||||
| 1000 | |||||
| 1030 | |||||
| 1100 | |||||
| 1130 | |||||
| 1200 | |||||
| 1230 | |||||
| 1300 | |||||
| 1330 | |||||
| 1400 | |||||
| 1430 | |||||
| 1500 | |||||
| 1530 | |||||
| 1600 | |||||
| 1630 | |||||
| 1700 | |||||
| 1730 | |||||
| 1800 |
SC1003
Introduction To Computational Thinking & Programming
SC1004
Linear Algebra For Computing
SC1005
Digital Logic
SC1006
Computer Organisation & Architecture
SC1007
Data Structures & Algorithms
SC1008
C & C++ Programming
SC1104
Linear Algebra For Computing
SC1124
Math 2: Discrete Structures For Computing
SC1302
Ethics
| Mon | Tue | Wed | Thu | Fri | |
|---|---|---|---|---|---|
| 930 | 10020 SEM (FTA1) 0930-1120 Mon LHN-TR+14 Wk2-13 | ||||
| 1000 | |||||
| 1030 | |||||
| 1100 | |||||
| 1130 | |||||
| 1200 | |||||
| 1230 | |||||
| 1300 | |||||
| 1330 | COMMON LEC (SCL1) 1330-1520 Wed TAISPSPACE | ||||
| 1400 | |||||
| 1430 | |||||
| 1500 |