Big Data & AI

Have you ever thought about how we measure information, or why some files take up less space than others?

That’s where Information Theory comes in. It helps us understand how information works, and forms the basis for things like data compression, secure communication, and even modern AI systems.
In this masterclass, we’ll start from the very basics of Information Theory and build up step-by-step toward powerful algorithms like Huffman Coding, all explained intuitively and with real-world relevance.

Whether you're from a CS background or just curious about the science behind efficient communication, this session will help you grasp the core ideas in a simple and engaging way.

What you will learn:

• What is information? Learn how we define and measure information using concepts like entropy.
• Shannon’s Pioneering Work: Understand the foundation laid by Claude Shannon that changed the world of communication.
• Redundancy & Efficiency: Discover how we represent information more efficiently using probabilistic insights.
• Huffman Coding Explained: Dive into one of the most elegant and widely used compression algorithms.
• Real-Life Applications: See how these concepts power file compression, data transmission, and even machine learning.

Prerequisites:
No prior knowledge is required — just curiosity! We’ll start from scratch, making this session perfect for students, developers, researchers, and AI enthusiasts looking to build strong intuition around how information works.
Whether you're interested in compression, cryptography, or just foundational computer science concepts, this masterclass will give you a solid starting point and a sense of wonder for how bits and bytes carry meaning.
-- Join us to decode the language of information! Reserve your seat now.
Bonus: Attendees will receive FREE CERTIFICATE of participation.

Today, you can type "a cat dressed as an astronaut riding a bicycle on Mars", and in seconds, an AI generates that image — in stunning detail.
Tools like DALL·E, Stable Diffusion, and Midjourney feel like magic. But behind this modern marvel lies a fascinating journey, and it all began with one humble idea:
What if machines could learn to imagine? That’s where Variational Autoencoders (VAEs) come in. Before GANs and diffusion models took over, VAEs laid the groundwork for machines to learn compressed representations of data and use them to generate entirely new content.
In this beginner-friendly masterclass, we’ll explore the complete journey — from the basics of autoencoders to how VAEs work, what problem they solve, and why they matter.
What you will learn:

• Autoencoders 101: What they are, how they work, and how they compress data.
• Different types of autoencoders — and why we needed something more.
• The core idea behind Variational Autoencoders (VAEs)
• How VAEs help machines generate new, creative outputs
• How they connect to today’s tools like image generators and anomaly detectors
• Step-by-step breakdown of VAE’s architecture, training process, and real-world uses

Prerequisites:
Basic understanding of machine learning and neural networks is helpful, especially the idea of encoder-decoder architecture.
Whether you’re a student, AI enthusiast, or just curious about how machines learn to see and create, this session will help you understand one of the most important steps in the journey of generative AI.
Bonus: Attendees will receive FREE CERTIFICATE of participation.