This course is designed to equip you with the essential skills and practical knowledge to harness machine learning techniques for accelerating materials discovery and design. Specifically tailored for students interested in materials science, chemistry, physics, and engineering, it provides hands-on experience with core and advanced machine learning methods-including neural networks, optimization strategies, and generative modeling-to tackle real-world materials science problems. By mastering these data-driven approaches, you'll enhance your research capabilities, prepare for cutting-edge industry roles, and lay a strong foundation for future coursework or careers at the intersection of artificial intelligence and materials innovation.
| AUs | 2.0 AUs |
| Grade Type | |
| Prerequisite | MS0003, MS1008, BG2211, CB0494, CH2107, CV0003, CV1014, SC1003, SC1015, EE0005, EE1005, MA0218, MA1008, IE0005, IE1005 |
| Not Available To Programme | |
| Not Available To All Programme With | |
| Not Available As BDE/UE To Programme | |
| Not Available As Core To Programme | |
| Not Available As PE To Programme | |
| Mutually Exclusive With | |
| Not Offered As BDE | |
| Not Offered As Unrestricted Elective | Yes |
| Exam |
Available Indexes
| Mon | Tue | Wed | Thu | Fri | |
|---|---|---|---|---|---|
| 930 | |||||
| 1000 | |||||
| 1030 | |||||
| 1100 | |||||
| 1130 | |||||
| 1200 | |||||
| 1230 | |||||
| 1300 | |||||
| 1330 | |||||
| 1400 | |||||
| 1430 | |||||
| 1500 | |||||
| 1530 | |||||
| 1600 | |||||
| 1630 | |||||
| 1700 | |||||
| 1730 | |||||
| 1800 |
Other Relevant Mods
MS1008
Introduction To Computational Thinking
MS1011
Materials Matter
MS1013
Materials Chemistry I
MS1016
Thermodynamics Of Materials
MS1017
Introduction To Materials Science
MS2012
Introduction To Manufacturing Processes
MS2015
Mechanical Behaviour Of Materials
MS2016
Introduction To Metallurgy
MS2083
Laboratory On Structure-Property Relationship In Polymers