Programme Parameters
Graduate / Intermediate
4 Weeks (40 Hrs)
Online (e-LMS)
• A basic understanding of the subject area and fundamental programming or scientific concepts.
• A laptop or desktop with a stable internet connection.
• Willingness to complete assignments and the capstone project.
About This Course
Generative AI and Intellectual Property Rights tackles one of the most consequential and unsettled areas in technology law. You examine the hard questions generative models raise: whether training on copyrighted data is infringement, who — if anyone — owns AI-generated output, how authorship and patent rules apply, and how licensing and attribution should work. The course maps the fast-moving legal landscape across jurisdictions and the leading cases shaping it, alongside the practical risk-management steps organisations can take now. You finish able to reason clearly about IP risk and rights in generative-AI work. A verified e-Certificate of competency and e-Marksheet from the Deep Science & Technology Consortium.
🎯 Program Aim
This course examines the intellectual-property questions raised by generative AI — copyright of training data and outputs, authorship, ownership and the evolving legal landscape.
📋 Course Objectives
1. Explain how copyright applies to training data and outputs.
2. Analyse authorship and ownership of AI-generated work.
3. Understand patent and licensing implications.
4. Compare the evolving legal landscape across jurisdictions.
5. Apply practical IP risk management to AI projects.
👥 Who Should Enroll?
• Legal, IP and compliance professionals
• AI product and content leaders
• Creators and businesses using generative AI
• Students of technology law and policy
🚀 Key Learning Outcomes
• The ability to reason about IP in generative AI.
• An IP risk-assessment approach.
• Fluency in a fast-moving legal debate.
• A verified e-Certificate of competency and e-Marksheet from the Deep Science & Technology Consortium.
💎 What You'll Gain
Live & Recorded Sessions
Lifetime access to class recordingse-Certificate on Completion
Cryptographically verified credentialPost-Programme Support
Direct access to mentors & councilHands-On Experience
Notebooks, real-world code & datasetsCurriculum Outline
NLP Foundations, Linguistics, and Generative AI & Intellectual Property Rights Fundamentals
Analyze the fundamentals of natural language processing and its applications in intellectual property rights • Develop a comprehensive understanding of linguistics and its role in generative AI • Evaluate the current state of generative AI and its implications for intellectual property rights
Text Preprocessing, Tokenization, and Feature Engineering
Implement text preprocessing techniques such as tokenization, stemming, and lemmatization • Design and develop feature engineering pipelines for NLP tasks • Configure and optimize text preprocessing workflows for improved model performance
Classical NLP Models and Statistical Methods
Apply classical NLP models such as n-gram models and Hidden Markov Models to real-world problems • Develop and evaluate statistical methods for NLP tasks such as sentiment analysis and topic modeling • Analyze and compare the performance of different classical NLP models and statistical methods
Deep Learning Architectures for Generative AI & Intellectual Property Rights
Design and implement deep learning architectures such as recurrent neural networks and transformers for generative AI tasks • Develop and train generative models such as language models and text generators • Evaluate and optimize the performance of deep learning architectures for generative AI tasks
Transformers, LLMs, and Attention Mechanisms
Implement and apply transformer architectures such as BERT and RoBERTa to NLP tasks • Develop and evaluate large language models such as LLaMA and PaLM • Analyze and compare the performance of different transformer architectures and attention mechanisms
Model Evaluation, Fine-Tuning, and Optimization
Evaluate and compare the performance of different NLP models using metrics such as accuracy and F1-score • Fine-tune and optimize NLP models for improved performance on specific tasks • Develop and implement model optimization techniques such as hyperparameter tuning and model pruning
Production NLP Systems, APIs, and Deployment
Design and develop production-ready NLP systems and APIs • Deploy and manage NLP models in cloud-based environments such as AWS and Google Cloud • Configure and optimize NLP systems for scalability and reliability
Technical Specifications
| Parameter | Requirement |
|---|---|
| Covered Tool / Platform | Python |
| Covered Tool / Platform | TensorFlow |
| Covered Tool / Platform | PyTorch |
| Covered Tool / Platform | scikit-learn |
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