- Level -Intermediate - Choice - Specialisation
- Exploring AI applications in language processing, translation, phonetics, and linguistic analysis.
- Prerequisites -Introduction to AI or any foundational AI course
- Assessments - Micro Assessment weekly, Full Assessment last week.
Week 1 - AI in Natural Language Processing (NLP) Fundamentals
Learning OutcomeStudents will understand the core AI techniques used in linguistic analysis.
1.1 Introduction to NLP and Linguistic Structure Analysis.
1.2 AI Models for Syntax, Semantics, and Sentiment Analysis.
1.3 Tokenization, Part-of-Speech Tagging, and Named Entity Recognition (NER).
Practical Component
Implementing NLP techniques to analyze and process text data.
Week 2 - AI in Speech Recognition and Phonetics
Learning OutcomeStudents will understand how AI processes and analyzes speech.
2.1 AI-Based Phoneme Recognition and Pronunciation Analysis.
2.2 AI for Speech-to-Text Conversion and Real-Time Transcription.
2.3 Handling Accents, Dialects, and Background Noise in Speech Processing
Practical Component
Training AI models for speech recognition with real-world datasets.
Week 3 - AI in Language Translation and Cross-Language Understanding
Learning OutcomeStudents will explore AI-driven language translation and multilingual AI applications.
3.1 Neural Machine Translation (NMT) and Transformer Models.
3.2 AI for Code-Switching and Bilingual Speech Processing.
3.3 Context-Aware AI Translation and Sentiment Adaptation.
Practical Component
Developing and testing AI-based language translation models.
Week 4 - AI in Text Generation and Conversational AI
Learning OutcomeUnderstanding AI-driven language models for text generation and chatbots.
4.1 AI Models for Automatic Text Generation and Summarization
4.2 AI in Conversational Agents and Chatbots.
4.3 Ethical Considerations in AI Language Generation.
Practical Component
Creating an AI-powered chatbot or text generation model.
Week 5 - AI in Linguistic Preservation and Dialect Analysis
Learning OutcomeExploring AIs role in preserving endangered languages and dialect research.
5.1 AI for Reviving and Preserving Endangered Languages.
5.2 AI in Dialect Identification and Linguistic Pattern Recognition.
5.3 AI in Text-to-Speech (TTS) and Speech Synthesis for Minority Languages.
Practical Component
Training AI to recognize and generate speech for lesser-known languages.
Week 6 - Project and Assessment
Learning OutcomeStudents will apply AI techniques in a linguistic research project.
6.1 Selecting a Linguistic Topic and Data Collection.
6.2 Model Development, Self and Peer Assessment
6.3 Final Presentations and Demonstrations.
Practical Component
AI-powered linguistic analysis presentation