Core Module Information
Module title: Machine Learning for Conversational AI

SCQF level: 11:
SCQF credit value: 20.00
ECTS credit value: 10

Module code: SET11124
Module leader: Yanchao Yu
School School of Computing, Engineering and the Built Environment
Subject area group: Computer Science


Description of module content:

Conversational AI has attracted increasing attention in academia and industry in the past five years. There have been over 100 companies working on voice-AI or Conversational AI-based products, including the world’s most valuable brands (e.g. Apple, Google, Amazon, Huawei, etc.) and start-ups (e.g. Emotech, Alana). On the other hand, there are global competitions organised by Amazon, which focus on state-of-the-art technologies in diverse conversational AI tasks, e.g. social conversation and task-oriented conversation. This module can help students learn about conversational AI, related NLP and ML/DL methodologies, and state-of-the-art research topics. It also can provide students with the experience of team-level project work on realistic human daily problems, which offers a good insight into academic and industrial perspectives.

The module covers the following topics: • Data types and formats: numerical and time series, graph, textual, unstructured, • Data sources and interfaces: open data, APIs, social media, web-based • NoSQL databases such as document (MongoDB), graph and key-value pair • Techniques for dealing with large data sets, including Map Reduce • Developing Data-Driven Applications in Python The Benchmark Statement for Computing specifies the range of skills and knowledge that should be incorporated in computing courses. This module encompasses cognitive skills in Computational Thinking, Modelling and Methods and Tools, Requirements Analysis and practical skills in specification, development and testing and the deployment and use of tools and critical evaluation, and providing useful generic skills for employment.

Learning Outcomes for module:

1 Critically evaluate the tools and techniques of Conversational AI, i.e. language understanding and generation, dialogue management, and multimodal interaction.
2 Select and apply a range of specialised data, tools and techniques for Conversational AI, i.e. language understanding and generation, dialogue management, and multimodal interaction.
3 Experiment with specialised techniques for complex real-world problems, such as Natural Language Processing and machine learning.
4 Design, develop, and critically evaluate data-driven conversational applications

Full Details of Teaching and Assessment
2023/4, Trimester 2, FACE-TO-FACE,
Occurrence: 001
Primary mode of delivery: FACE-TO-FACE
Location of delivery: MERCHISTON
Member of staff responsible for delivering module: Yanchao Yu
Module Organiser:

Student Activity (Notional Equivalent Study Hours (NESH))
Mode of activityLearning & Teaching ActivityNESH (Study Hours)
Face To Face Lecture 20
Face To Face Guided independent study 140
Face To Face Project Supervision 10
Face To Face Practical classes and workshops 30
Total Study Hours200
Expected Total Study Hours for Module200

Type of Assessment Weighting % LOs covered Week due Length in Hours/Words
Project - Practical 30 1,2 6 HOURS= 00.00, WORDS= 1000
Project - Practical 70 3,4 14/15 HOURS= 00.00, WORDS= 3500
Component 1 subtotal: 100
Component 2 subtotal: 0
Module subtotal: 100

Indicative References and Reading List - URL:
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