Computer Science | AI for Tomorrow’s World

About the course

Everyday, everytime, each news article on AI is pointing towards the same deliberate question: How will AI change the future world? 

According to an article from Tech Target, industries such as education, healthcare, law, finance and transportation will have the biggest impact of AI and its technological advancement. On the other hand, a featured insight by McKinsey quotes “ AI is here to stay. To outcompete in the future, organizations and individuals alike need to get familiar fast”.

When it comes to the Intelligence Age, we are in the midst of a revolution. What does this mean for you, who will enter the workforce – tech or non-tech, probably 5 or 7 or 10 years later? – “Its coming and gear up now!”

AI for Tomorrow’s World is an immersive live course that will provide a comprehensive grounding in artificial intelligence and machine learning, starting from scratch. This is meant to build sufficiently strong foundations for you to seriously make a foray into the field. The course will introduce concepts, applications, and challenges of Machine Learning and Artificial Intelligence and is designed to guide you from the theoretical underpinnings of ML algorithms to hands-on implementation and real-world challenges. 

Throughout the course, you will explore key topics such as model explainability, data privacy, security concerns, and the limitations of AI systems. You will learn both basic theory and implementation, along with ethical considerations and security problems. You will delve into the concept of “black-box” models, where the internal workings of AI models are opaque and difficult to interpret. Finally, you will examine the concept of “breaking” large language models (LLMs) by testing their responses to adversarial inputs and outliers.

As the recent Nobel prizes in Physics and Chemistry have demonstrated, AI/ML has become ubiquitous across disciplines. The very nature of knowledge has changed. To be a responsible and informed citizen in this new world, one must have a solid grasp of what AI really means, and the pitfalls involved. This course is your springboard for the same. By the end of the course, you will be equipped with both the knowledge and practical skills to work with modern AI techniques and critically assess their implications in real-world contexts.

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Eligibility

• High school students from India and across the world are eligible to apply.
• Students should be entering or in grades 9-12 as in summer 2025.
• Students should be comfortable with written & spoken English, which will be the medium of instruction for the courses.
• Students should have consistent access to the Internet and a computer that aligns with the recommended minimum specifications.
• Students should have a good and consistent academic record.

Who is this course for?

This course is for high schoolers who have an interest in computer science and mathematics. It is for anyone who wants to be upbeat with the latest knowledge and advancements in the field of ML & AI.

 Prerequisite: A basic understanding of computer science and mathematics. You need not have programmed extensively before, but you should be “computer literate” and willing to get your hands dirty!

What do I take away?

1. Gain a basic but clear understanding of AI/ML and distinguish between AI, Machine Learning, and Deep Learning.
2. Engage with simple code in the domain and recognize the significance of AI in various real-world applications.
3. Delve into the concept of “black-box” models in machine learning.
4. Learn techniques to diagnose and address common mistakes in AI models.
5. Evaluate the importance of model correctness and explainability in AI applications.
6. Reason about ethical and security issues with implementations
7. Build and train a simple Convolutional Neural Network for image classification.
8. Design and experiment on your own.

Programme Details

Week	Lecture Module	Project Module
Week 1	Introduction to AI & ML Explore the foundations of ML and AI, including key concepts, historical milestones, and the current landscape. Supervised vs. unsupervised learning Regression & classification Role of algorithms	Research on applications of AI Research on real-world AI applications across industries such as healthcare, finance, education and much more.
Week 2	Black Boxes & mistakes Unlock black box models and decode its characteristics of opacity & complexity Neural networks Decision making Pitfalls & complexities	Examine out-of-distribution data  Examine out-of-distribution data in an example network – analyze situations where the model encounters data that is significantly different from what it was trained on.
Week 3	Correctness, Explainability, Privacy, and Security  Focus on some of the critical issues surrounding the deployment of AI systems, particularly regarding model correctness, explainability, privacy, and security. Model accuracy and interpretability Data leakage and unauthorized access Security of AI systems	Break Large Language Models Understand and practice the concept of “breaking” large language models (LLMs) by testing their responses to adversarial inputs and outliers.
Week 4	Programming Simple AI Modules Apply your theoretical knowledge by implementing basic AI algorithms Data preprocessing Training models Evaluating model performance	Classify Convolutional Neural Networks  Using Convolutional Neural Networks (CNNs), build a simple image classification model.
Week 5	Counselling:  Get a chance to ask questions to the faculty and the mentor and get their answers and perspective.  You are encouraged to ask questions to the faculty around the following aspects: What are some related fields that can be explored based on this course topic? What are the future trends in the related fields? How can the course learning help the student enhance their college applications?	Mentoring: You are encouraged to ask questions to the mentor around the following aspects: What are some sub-topics to learn/practice beyond the course? What is the scope of the course topic if the student wants to pursue/study it further? What are some personal projects that can be taken up outside of the course based on the knowledge gained in the course?

Capstone Project

As a capstone project, you will choose between customized CNN for image classification or study of breaking LLMs. Customizing a CNN will allow you to experiment with different architectures, layers, and hyperparameters to optimize your model for a specific dataset or task. Breaking LLMs will help you test their limitations, vulnerabilities, weaknesses and biases under adversarial conditions.

Faculty

Debayan Gupta is currently an Assistant Professor of Computer Science at Ashoka University, where he teaches a course on security and privacy as well as an introductory programming class. He is also a visiting professor and research affiliate at MIT and MIT-Sloan.

Before coming to Ashoka, Debayan held an Extraordinary Faculty position in the Department of Electrical Engineering and Computer Science at MIT, where he taught courses like 6.042, 6.006, and 6.046. He has a PhD from Yale and a bachelor’s degree from St. Xavier’s College, Kolkata.

Debayan’s primary areas of interest include secure computation, cryptography, and privacy. He also occasionally dabbles in number theory, complexity theory, robotics, and machine learning (and, on rare occasions, economics). He has helped start a number of companies in India and abroad, and as such, holds board positions in a number of start-ups. He also consults for and advises companies on cybersecurity, helping c-suite individuals understand and mitigate cyber-risk.

Grading, assessments, certification and much more

Grading, Assessments & Certification

All Ashoka Horizons courses offer a certificate on satisfactory completion of the programme. 

• Weekly project progress – 20%
• Completion of project – 70%
• In-class participation and attendance – 10%

Class participation will be assessed based on your active engagement in live sessions, contributions to discussion forums, and involvement in Teaching Fellow-led activities. 

Achieve More…with Horizons:

• Successful Course Completion Certificate
• Letters of Academic Achievement to top 3 students based on exceptional performance 
• Potential opportunity for an internship*

*For select students, subject to discretion of the faculty

IT Requirements

This course is administered through an online platform. Students are expected to have a foundational understanding of computer usage, including but not limited to sending emails and conducting Internet searches. Consistent access to the Internet and a computer that aligns with the recommended minimum specifications are also requisite for participation in the course.