This course is intended to introduce to you the concepts of Natural Language Processing (NLP) from the fundamentals till the latest technologies and enable you to apply NLP techniques in real-world projects. It will introduce several major popular state-of-the-art NLP techniques with a focus on attention-based deep learning models. You would need to have exposure to programming skills (Python). An important takeaway would be the translation of theoretical concepts learnt into practical hands-on applications by running simulations on real-life datasets such as machine translation or text summarization. With the emergence of AI-based generative tools such as ChatGPT, NLP is becoming a highly valued topic in several research and development laboratories/sectors, hence this course will equip you with useful industry-relevant skills. In addition, this course will be relevant across 5 MSc programmes (CME, CCA, ET, PE and SP) since students taking this course will be equipped with the fundamental skillsets for machine learning and deep learning algorithms that can be applied in various contexts beyond NLP.
| AUs | 3.0 AUs |
| Grade Type | |
| Prerequisite | |
| 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 | |
| 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 |
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