AI Masters Curriculum

Phase 1: Blended Program (First 18 Months)

The first 18 months provide a comprehensive overview of various disciplines, blending AI, data, cloud, coding, game design, and leadership topics to prepare students with a strong foundation.

Year 1: Advanced Coding + Core AI Concepts (Months 1–12)

Months 1–6: Advanced Coding Techniques and AI Foundations

• Advanced Coding with LeetCode & Interview Prep (Google, Tesla, OpenAI, etc.):

  • Weekly coding practice sessions from LeetCode and top tech company interview question sets.

  • Topics: Data structures (arrays, linked lists, hashmaps, trees, graphs), dynamic programming, recursion, bit manipulation, sorting algorithms.

  • Weekly mock coding interviews.

  • Problem-solving with real-time feedback and guidance.

• AI Foundations:

  • Introduction to AI & Machine Learning:

    • History of AI, Types of AI (Narrow AI, General AI), AI vs. ML vs. Deep Learning.

    • Overview of Machine Learning algorithms (supervised, unsupervised, reinforcement learning).

    • Real-world application: Predictive models using linear regression, logistic regression, and classification algorithms.

  • Python for AI:

    • Advanced Python programming with a focus on AI applications.

    • Libraries: NumPy, Pandas, Scikit-learn, Matplotlib, TensorFlow, Keras.

    • Real-world application: Build a predictive model using Scikit-learn.

  • AI Algorithms and Data Preprocessing:

    • Introduction to AI algorithms: Decision trees, SVM, k-nearest neighbors, clustering (k-means).

    • Data collection, data cleaning, feature engineering.

    • Real-world project: Build a machine learning model to predict stock prices.

Months 7–12: Advanced AI Techniques & Cross-Disciplinary Learning

• Deep Learning & Neural Networks:

  • Introduction to neural networks and deep learning.

  • Architectures: Feedforward, convolutional neural networks (CNN), recurrent neural networks (RNN), and LSTMs.

  • Transfer learning and pre-trained models.

  • Real-world project: Build and train an image classification model using CNN.

• Natural Language Processing (NLP):

  • Text processing, tokenization, embeddings (Word2Vec, GloVe).

  • Transformers and advanced models like BERT, GPT.

  • Real-world project: Build an AI chatbot using GPT-3 or similar models.

• Cloud AI:

  • Using cloud platforms (AWS, GCP, Azure) for AI training.

  • Setting up cloud environments, data pipelines, and deploying AI models on the cloud.

  • Real-world project: Deploy a machine learning model in the cloud.

• Blending Topics from Other Master’s Tracks (Months 7–12):

  • Data Science & AI: Data manipulation, visualization, and analytics for AI projects.

  • Web & Mobile Development: Building AI-powered web/mobile apps.

  • Game Design & VR: Introduction to AI in game development and virtual environments.

  • Leadership & Startup: How to pitch and build AI-driven startups and products.

  • Cloud & DevOps: Managing scalable AI systems in the cloud, MLOps.

Year 2: Specialization in AI (Months 13–18)

Months 13–18: Specialization in AI

• Reinforcement Learning (RL):

  • Concepts of RL: agents, states, actions, rewards.

  • Policy gradients, Q-learning, deep reinforcement learning (Deep Q Networks).

  • Applications: Autonomous systems, robotics.

  • Real-world project: Build an AI agent for game play or robotic task automation.

• AI Ethics, Safety, and Governance:

  • Ethical considerations in AI (bias, fairness, privacy).

  • Implementing safety measures in AI applications (e.g., adversarial attacks).

  • Responsible AI frameworks and governance policies.

  • Real-world scenario analysis: Ethical challenges in AI deployment (e.g., facial recognition, autonomous driving).

• Computer Vision (CV):

  • Advanced computer vision algorithms (object detection, segmentation, image generation).

  • Techniques: YOLO, Faster R-CNN, GANs.

  • Real-world project: Build an AI system for real-time object detection in self-driving cars.

• MLOps (Machine Learning Operations):

  • Creating and managing ML pipelines.

  • Automating model deployment, monitoring, and scaling in production.

  • Tools: Docker, Kubernetes, CI/CD for machine learning models.

  • Real-world project: Deploy a full AI pipeline for image or text classification on the cloud.

• AI in Robotics and Automation:

  • AI for autonomous systems and robotic process automation.

  • Integrating AI with hardware (Raspberry Pi, IoT).

  • Real-world project: Build an AI-driven autonomous robot.

Phase 2: Specialization and Real-World Applications (Months 19–36)

Months 19–30: Specialization in Advanced AI Topics

In this phase, students pick a focus area from various AI subfields and work on advanced projects.

Deep Learning & Advanced Neural Networks (Option 1)

• Advanced architectures: Transformer models, GANs, autoencoders.

• Building and training large-scale models.

• Real-world project: Build a generative model for creating synthetic data or art.

Natural Language Processing and Conversational AI (Option 2)

• Building advanced NLP systems (machine translation, sentiment analysis, chatbots).

• Large language models and fine-tuning pre-trained models (e.g., GPT-4).

• Real-world project: Create a virtual assistant or sentiment analysis engine.

Computer Vision and AI for Autonomous Vehicles (Option 3)

• Advanced vision techniques (3D object detection, pose estimation, SLAM).

• AI for autonomous vehicles and drones.

• Real-world project: Build a computer vision system for an autonomous driving application.

AI in Healthcare and Biomedicine (Option 4)

• AI for medical imaging, drug discovery, and patient diagnostics.

• Using deep learning for predictive healthcare analytics.

• Real-world project: Develop an AI solution for medical imaging or diagnostics.

Phase 3: 6-Month Live Project (Months 31–36)

Live Capstone Project (6 Months)

• Real-World AI Project: Students work on a live project, either in collaboration with industry partners or on an AI-driven startup concept.

• Team Collaboration: Students collaborate in teams, using AI to solve complex, real-world problems in healthcare, autonomous systems, finance, or other domains.

• Project Examples:

  • Building an AI-driven solution for urban traffic management.

  • Developing an AI system for fraud detection in financial services.

  • Building a conversational AI system for enterprise customer support.

  • Creating an AI-powered robotic assistant for industrial automation.

• Project Phases:

  • Phase 1 (Months 31–32): Problem definition, research, and solution design.

  • Phase 2 (Months 33–34): Building and testing the AI solution, iterative improvements.

  • Phase 3 (Months 35–36): Final deployment, evaluation, and presentation to stakeholders or investors.

Program Outcomes:

• Mastery in advanced AI techniques including deep learning, NLP, computer vision, and reinforcement learning.

• Proficiency in cloud-based AI deployment, MLOps, and large-scale machine learning systems.

• Extensive real-world experience through live projects, coding challenges, and team collaboration.

• Strong preparation for technical interviews at top tech companies like Google, Tesla, OpenAI, Facebook.

• Leadership and teamwork skills gained through collaboration and mentorship.