Master of Management Studies (M.M.S) Artificial Intelligence and Machine Learning for Business Syllabus - Mumbai University 2026

Course objectives

• 1 To familiarize students with foundational concepts, advantages, and disadvantages of Artificial Intelligence and Machine Learning.
• 2 To equip students with the skills to analyze deep learning techniques and their applications, particularly in Natural Language Processing (NLP).
• 3 To develop the ability to apply Generative AI for predictive modeling and informed decision-making in business contexts.
• 4 To foster an understanding of the practical deployment of AI technologies across core business functions such as Marketing, Finance, Human Resources, and Operations.
• 5 To cultivate the skills necessary to design effective implementation plans for machine learning concepts to address real-world business challenges across various industries.

Course outcomes

• CO1: To understand the concept of Artificial Intelligence and Machine learning, its advantages and disadvantages
• CO2: To analyze Deep learning its use cases and application of Natural language processing (NLP)
• CO3: To apply the knowledge of Generative AI, for Predictive modelling and smarter decisions.
• CO4: To evaluate the practical deployment of Artificial Intelligence across core business functions like Marketing, Finance, Human Resources and Operations.
• CO5: To design an effective plan to implement key machine learning concepts and their relevance to solve real-world business problems in various industries

Unit 1 CO1 · 4 hours

Introduction to Artificial Intelligence and Machine Learning History and Evolution of AI, Forms of Machine Learning – Supervised, Unsupervised, Reinforced learning, Advantages and Disadvantages

Unit 2 CO2 · 6 hours

Deep Learning and Neural Networks, Natural Language processing (NLP). Deep learning Use cases, Neural Networks in Deep learning, Layers, Forward and Backward propagation. Applications for NLP

Unit 3 CO3 · 6 hours

Generative AI , Data Visualization and Predictive Analytics. Working of Generative AI , Use cases and examples of GenAI, Benefits, Limitations and Concerns of GenAI, Data Visualization , AI for Predictive Analytics and Decisions.

Unit 4 CO4 · 5 hours

Exploring the practical deployment of Artificial Intelligence across core business functions— Marketing, Finance, Human Resources, and Operations. One can examine how AI-driven tools and techniques are transforming customer engagement, financial forecasting, talent acquisition, performance management, supply chain optimization, and decision-making processes. Limitations of Applications in these domains without AI and the advantages of AI to improve these applications.

Unit 5 CO5 · 4 hours

Introduction of key machine learning concepts and their relevance to solving real-world business problems. Through case studies, exploring how machine learning models can be applied in areas such as customer segmentation, sales prediction, fraud detection, and process automation. Interpreting model outcomes and aligning ML solutions with business objectives.

Unit 6 CO2, CO3, CO4, CO5 · 5 hours

AI Applications in various industries and across the sector. In Health care, Retail, Transportation, - Autonomous vehicle, Education and Agriculture, in Smart Cities, AI Ethics and Future Trends. Emerging trends in AI and ML case studies Emerging Trends of Artificial Intelligence and Machine Learning with case studies

Textbooks

• 1 Teik Toe Teoh, Yu Jin Goh, Artificial Intelligence in Business Management (Machine Learning: Foundations, Methodologies, and Applications, Kindle Edition
• 2 Steven Finley, Artificial Intelligence and Machine Learning for Business: A No- Nonsense Guide to Data Driven Technologies, Relativistic; 2nd edition
• 3 Doug Rose, Artificial Intelligence for Business, Pearson; 2nd edition

Reference Books

• 1 Aditya Singh Yadav, Ms Sharmila Singh, AI & ML for Business, Ibrj Publications
• 2 Sandeep Kumar Panda, Vaibhav Mishra, R. Balamurali, Ahmed A. Elngar,