Advanced AI Program: Build Industry-Ready Products

• Get aligned with program goals, outcomes, and expectations

• Learn the art of defining the right problem statement

• Learn how to address the problem and build solutions

• Learn to acknowledge the use cases to provide a seamless experience with your solutions

• Reconnect with core AI ML concepts

• Kick-off to your industry project

This is where you move from using models to truly understanding and improving them.

What you'll explore:

• Neural networks & backpropagation

• Loss functions & activation functions (ReLU, sigmoid, softmax)

• Regularization: L1, L2, dropout

• Optimization methods: SGD, Adam, RMSprop, learning rate scheduling

• Bias-variance tradeoff & performance evaluation (F1, AUC, confusion matrix)

• Hyperparameter tuning: grid vs random search

This module connects the dots between math, code, and model behavior.

What you'll explore:

• AI ML development workflow: from raw data to model deployment

• AI training pipelines and iterative feedback loops

• Understanding convergence and loss behavior

• Transformers and the attention mechanism

• Optimization methods: gradient descent, momentum, Adam, learning rate schedules

What you'll explore:

• What are LLMs? Differences from traditional ML models

• Applications of LLMs: summarization, Q&A, translation, sentiment analysis

• Anatomy of a transformer: attention mechanism, self-attention, multi-head attention

• Encoder-decoder vs decoder-only architectures (BERT, GPT, T5)

• Pretraining vs fine-tuning: zero-shot, few-shot, supervised fine-tuning

• Tokenization strategies: byte-pair encoding (BPE), WordPiece

• Embeddings and positional encodings

• Responsible usage: risks of hallucination, misuse, and mitigation strategies

• Emerging trends: Retrieval Augmented Generation (RAG), LoRA, Instruction tuning

What you’ll explore:

• Fundamentals of Generative AI: How machines create

• GANs (Generative Adversarial Networks): architecture, discriminator vs generator

• VAEs (Variational Autoencoders): latent space and reconstruction

• Diffusion Models: Denoising Diffusion Probabilistic Models (DDPMs)

• Use cases: marketing, design, music, game development, healthcare

• Multimodal GenAI: models like DALL·E, Stable Diffusion, RunwayML

• Text-to-image, text-to-video, and audio synthesis

• Ethical use of GenAI: bias, copyright, and consent

• How companies use GenAI (real case studies)

What you’ll explore:

• Zero-shot, few-shot prompting

• Prompt templates & structure

• Prompt evaluation: effectiveness, consistency

Take prompting to the next level by gaining control over tone, style, and conversation flow.

What’s inside:

• System prompts, user instructions

• Multiturn conversation logic

• Controlling response tone & format

• Debugging & refining prompt chains

End with a bang — a real-world project and a fun, fast-paced hackathon.

This Wrap-up includes:

• Industry Project: Ideation → Milestones → Mentor check-ins → Final demo

• Prompt Hackathon or GenAI Challenge

• Final presentation + portfolio showcase

• Letters of Recommendation & Certification

• Career path discussion (college apps, internships, future tech skills)