| CODE | LLT2520 | ||||||
| TITLE | Introduction to Computational Models for Language Data | ||||||
| UM LEVEL | 02 - Years 2, 3 in Modular Undergraduate Course | ||||||
| MQF LEVEL | 5 | ||||||
| ECTS CREDITS | 4 | ||||||
| DEPARTMENT | Institute of Linguistics and Language Technology | ||||||
| DESCRIPTION | This study-unit will provide a gentle introduction to computational modelling concepts using the Python programming language. It is intended for students with a basic level of mathematics and who intend to use computational modelling for solving natural language related problems. Topics that this study-unit will focus on include: - a general introduction to computational modelling; - evaluation of the outcomes of such models; - various data preprocessing techniques; - various models and algorithms such as decision trees, artificial neural networks, and nearest neighbours algorithms. Lectures will focus on how to use computational modelling techniques in order to solve natural language related problems such as text classification and tagging. Practical exercises will be provided. Attendance is mandatory unless excused by the lecturer. Study-unit Aims: - To provide a practical introduction to computational modelling concepts; - To encourage students in the language sciences to use computational models in projects they may carry out. Learning Outcomes: 1. Knowledge & Understanding: By the end of the study-unit the student will be able to: - Describe a number of different types of models; - Identify the correct type of model to use for different NLP tasks; - Identify suitable types of feature representations for text data; - Identify suitable types of evaluations for different types of models. 2. Skills: By the end of the study-unit the student will be able to: - Process data for training and evaluating a model; - Train models effectively; - Evaluate a trained model for an NLP task; - Tune the hyperparameters for a model. Main Text/s and any supplementary readings: - Daum茅, H. (2017). A course in machine learning (pp. 149-155). Hal Daum茅 III, available at: http://ciml.info/ - Jurafsky, D. (2000). Speech & Language Processing. Pearson Education India, available at: https://web.stanford.edu/~jurafsky/slp3/ |
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| ADDITIONAL NOTES | Pre-Requisite qualifications: Programming in Python Pre-Requisite Study-Unit: LLT2510 or equivalent |
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| STUDY-UNIT TYPE | Lecture and Practicum | ||||||
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| LECTURER/S | Marc Tanti |
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The University makes every effort to ensure that the published Courses Plans, Programmes of Study and Study-Unit information are complete and up-to-date at the time of publication. The University reserves the right to make changes in case errors are detected after publication.
The availability of optional units may be subject to timetabling constraints. Units not attracting a sufficient number of registrations may be withdrawn without notice. It should be noted that all the information in the description above applies to study-units available during the academic year 2025/6. It may be subject to change in subsequent years. |
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