Emerging Technologies


This course is aimed at providing an exposure to some of the latest upcoming digital technologies that will shape businesses and industries. This will provide a strategic insight into how new technologies are being integrated within new business models to deliver value for users. It will also have sufficient exposure to practical exercises especially in the area of utilising Machine Learning and Blockchain Applications. The main learning goals are:


- To develop a general awareness of the Technological Evolution primarily in the Digital Space and its final adoption by the Industry.

- An indepth understanding and practical experience of the applications of Machine Learning with a special focus on application scenarios of Image and Text Processing. This will be the main theme of their final project where the students will develop a business application on Machine Learning, implement a prototype and prepare a business strategy for a new startup.

- Have a deeper understanding about the Key Digital Technologies and how they are integrating with newer Business Models.

- An understanding of how “Enabling Technologies” allow for the creation of Tech Ecosystem Platforms where app developers and users can develop a myriad of applications and use cases. Eg, The Android OS leading to development of App Ecosystems, ODOO platform allowing for ERP ecosystems, Microsoft Hololens allowing for Virtual Reality Ecosystems.

- An overview of the following Technologies: Virtual Reality, 3D Printing, Self Driving Cars, IOT based systems.

- An indepth understanding and practical experience of Blockchain Technologies, Token Economies and its application in Web 3.0


General characterization





Responsible teacher

Ana Paula Ferreira Barroso, Aneesh Zutshi


Weekly - 2

Total - 28

Teaching language



This course is designed for all the students of Industrial Engineering who seek to develop their technical and business application skills. It does not require prior programming experience.


1. “An Introduction to Statistical Learning”, G. James, D. Witten, T. Hastie, and R. Tibshirani, 2021 : available online at https://www.statlearning.com/
2. "The Hundred-Page Machine Learning Book", A. Burkov, 2019, https://www.amazon.es/dp/199957950X?tag=hackr056-21&geniuslink=true
3. "Machine Learning for Hackers: Case Studies and Algorithms to Get You Started", D. Conway and J. M. White, 2012, https://www.amazon.es/dp/1449303714?tag=hackr056-21&geniuslink=true
4. "Pattern Recognition and Machine Learning (Information Science and Statistics)", Ch. M. Bishop, 2010, https://www.amazon.es/dp/0387310738?tag=hackr056-21&geniuslink=true
5. "Natural Language Processing with Python: Analyzing Text with the Natural Language Toolkit", S. Bird, E. Klein, and E. Loper, 2009, https://hackr.io/blog/best-machine-learning-books

Teaching method

The course comprises of 12 Theoretical + Practical (TP) Lectures of 2 hours each. During each class Theoretical Elements of the emerging technology is presented alongwith time for Practical activities that include Group Discussions, and development of their project for machine learning.

For the final work they identify an innovative Business Case using Machine Learning. The development of the project work includes technical development as well as business model innovation. The final presentation alongwith a video and report is presented at the end of the course.

The students also have a Test at the end of the course.

Approval occurs if the Final Mark is equal to or greater than 9.5 points.

Evaluation method

The course has a project work that includes a Presentation, preparation of a promotional video and a report. This project work has 70% of weightage. 

The final project is proposed as the main evaluation technique since it is able to encompass a variety of skills: Teamwork, Exploration, Engagement, Quality of Developed project, Presentation skills, and long term strategic thinking in terms of taking a technology to market.
The Assessment will be based on the overall performance of the team assessed through continuous class engagement and finally through the presentation of their final project.

The course also has a test that has 30% weightage.

Subject matter


Theoretical 1:20 (h)

Practical 1:30 (h)


Machine Learning

-        Key Concepts behind Artificial Intelligence, Machine Learning and Deep Learning.

-        Neural Networks – Concept and application

-        Various application scenarios in Industrial Automation, Big Data Analytics and IOT such as Image Recognition, Pattern Recognition, Predictive Analytics.

-        Understanding how machine learning algorithms work and can be applied.


-        Demonstration of Tools on Google Cloud Platform

-        Image Classification Example Using Vison AI.


Image Processing

-        Computer Vision and Machine Vision, and its applications such as industrial picking.

-        Demonstration of Google Cloud and how to use the platform for Image Classification.

-        A Discussion on building business scenarios based on Image Processing.

-        Medical Image Processing


-        An in-class exercise on Implementing Image Recognition and Image Classification system using Google Cloud.


Natural Language Processing

-        Concepts behind speech recognition, natural language understanding, and natural language generation.

-        Application scenarios for NLP in Industrial Context, Data Mining, Social Media Analytics

-        Demonstration of Google Cloud for simple NLP applications such as Sentiment Analysers.


-        An in-class exercise on Implementing Sentiment Analysis and Text Classification


Implementation of Machine Learning Using Tensorflow and Python

-        Introduction to Tensorflow platform and core libraries for machine learning.

-        The usage of Python API.

-        Simple application demonstrations in the context of Image Classification, Text mining

Introduction to Class Final Project

-        The final project will be developed by the students throughout the semester and will be finally presented in Class 12.

-        The project will consist of teams choosing either an Image, Text or Data Analytics based business use case using Machine Learning (ML)


Other applications of Machine Learning

-        Use in training Autonomous Vehicles. A detailed Case Study of Tesla

-        Reinforcement Learning

-        Conversational AI and how they improve on speech recognition, eg. Alexa

-        Generative Adverserial Networks – Its use for creating Deep Fakes

-        How to build next generation business models using AI.


-        Time for Groups to work on Class Final Project with Support provided


The evolution of Technologies.

-        Gartner Hype Cycle. Technology Readiness Levels (TRLs).

-        The Discussion on Successes and Failures of Technologies.

-        Discuss Key Failures like Google Glass, and a discussion on why certain promising technologies fail.


-        Time for Groups to work on Class Final Project with Support provided


How Digital Ecosystems are created.

-        The Dynamics of Digital Ecosystems explained through the following examples:

-        Android Application Ecosystem

-        Odoo ERP App Ecosystem

-        Ecosystem for 3D printer/ Virtual Reality objects

-        Blockchain based Ecosystems

-        Concepts behind Metcalfe’s Law, Network Effects and factors that enable an ecosystem to grow.

-        Pricing and Business Models within Digital Ecosystems. Concepts behind organic adoption.


-        Time for Groups to work on Class Final Project with Support provided


3D Printing

-        An overview of the improvements in printing quality, technology challenges, and application scenarios.

-        A discussion of the main 3D printing design platforms in existence.

-        Applications in Industry (eg. Airbus) and in Healthcare (Prosthetics on demand). Platforms for sale of 3D designs.


-        Time for Groups to work on Class Final Project with Support provided


Virtual Reality

-      Developments in the VR technology space, software like Unity, Godot, Unreal

-      Microsoft Hololens and Oculus with some examples of VR Applications.

-      Haptic Feedback suits and devices

-      Immersive Metaverses and their future.

-      A demonstration of Metaverse like Second Life, Decentraland and discussion on Businesses and Economic Opportunities existing today within metaverses

-      Facebook’s Strategy for the future of Metaverses.

-      A discussion on Foundational principles of metaverses and role of emerging Web 3 in terms of creation of Decentralised Metaverses.


-        Time for Groups to work on Class Final Project with Support provided


Practical Support class for ML Project Implementation

-        Each team will show their current status of their ML implementation and share the performance metrics such as accuracy achieved. Their performance metrics for evaluating the ML will be reviewed, and suggestions will be given to each team to improve their selected performance metrics if needed.

-        On the conclusion of their prototype, each team will be advised to identify how can this prototype be applied in their real life case scenarios. They will be supported for building strategies for commercial exploitation of their projects to build real startups.


Blockchain – Concepts and Philosophy

-        The need for a decentralised internet of Value : Web 3.0

-        The evolution of Bitcoin and Ethereum. – 1st Generation Blockchain

-        How Blockchain consensus and security works

-        Limitations of 1st generation blockchains in terms of Scalability, Speed and Governance Models

-        Practical Examples of Decentralised Asset Management: Creation of a Wallet, Seed phrases. Relationships between public keys and private keys.


-        Time for Groups to work on Class Final Project with Support provided


-        Focus on this class will be to develop a Business Model Canvas and as strategy for the commercial exploitation of their Class Project.


Presentation of Final Project

Each group will present their project, provide a live demonstration of their prototype, and explain their business plan for path to commercial exploitation. For high quality teams, additional support will be provided through the University Incubator including access to finance, technical resources and networks to commercialise their projects.


Next Generation Smart Contract Platforms.

-        Improvements in terms of Proof of Stake Consensus Mechanisms, Interoperability amongst Blockchains, Speed and Scalability, Smart Contract Development Interface, Governance Models and Tokenomics.

-        Overview of Solana Platform, Avalanche Platform, Harmony Platform

-        Languages and Developer Options

-        Decentralised Finance – Automated Lending Platforms, Decentralised Exchanges

In-Class Exercise

-        Create your own Wallet

-        Launch your own Token Exercise




-        Token Design and Use Cases

-        Social Tokens – Platforms like Rally and Chiliz

-        Tokenization of Real Estate – Example of Solidblock

-        Non Fungible Tokens (NFTs)

-        Applications of NFTs in Art, Intellectual Property, and its role in the future of a decentralised Metaverse.

In-Class Exercise

-        Launch your own NFT and NFT Collection

-        Define NFT Parameters, Royalties, etc. (1 hour)

-        Show all Promotional Videos by all teams developed as part of their project (30 minutes)


Programs where the course is taught: