TehSaadSaad Rizwan

Record No. 005 · AI Track

Technical Specialization

AI Engineering &
Machine
Intelligence

A structured progression from computing foundations and mathematics to classical machine learning, deep neural architectures, transformers, and production AI deployment.

View Curriculum Applied Projects
AI TRACK // 2026-2030
NUST SEECS · AI Roadmap
4
Core Phases
Python
Primary AI Stack
PyTorch
Deep Learning
Deploy
End-to-End Systems

First Principles

Mathematical grounding in linear algebra, multivariable calculus, and probability.

Model Architecture

Deep learning from perceptrons and CNNs through to self-attention and Transformers.

System Integration

Retrieval-Augmented Generation (RAG), vector embeddings, and tool-using agents.

Production Engineering

FastAPI services, Docker containers, evaluation metrics, and API deployment.

Curriculum

The 4 Core Phases

A systematic progression designed to bridge theoretical understanding with production-ready software engineering.

PHASE 01 · MONTHS 01–03

Foundations & Applied Math

Establishing algorithmic rigor, data manipulation fluency, and the foundational mathematics of machine learning.

  • Python Ecosystem: NumPy vectorized operations, Pandas data munging, Matplotlib/Seaborn analytics.
  • Mathematics for AI: Linear algebra (matrices, eigenvalues), multivariable calculus (gradients, chain rule), and probability distributions.
  • Data Structures: Algorithmic problem solving, complexity analysis (Big-O), search and graph traversal.
PHASE 02 · MONTHS 04–05

Classical Machine Learning

Transitioning from statistical mathematical formulations to predictive modeling and validation pipelines.

  • Supervised Learning: Linear & logistic regression, decision trees, random forests, SVMs, gradient boosting.
  • Unsupervised Methods: K-Means clustering, PCA dimensionality reduction, anomaly detection.
  • Model Evaluation: Cross-validation, precision/recall trade-offs, ROC-AUC, feature selection, regularization.
PHASE 03 · MONTHS 06–08

Deep Learning & Transformers

Designing, training, and fine-tuning neural architectures with PyTorch across vision and natural language processing.

  • Deep Learning Fundamentals: Backpropagation from scratch, activation functions, optimizers (Adam, SGD).
  • Computer Vision: Convolutional Neural Networks (CNNs), transfer learning, feature representation.
  • NLP & Transformers: Tokenization, word embeddings, self-attention mechanisms, BERT and GPT architectures.
PHASE 04 · MONTHS 09–12

AI Systems & Production Deployment

Packaging AI models into robust, scalable software services with APIs, databases, and monitoring.

  • GenAI & RAG: Vector databases (Chroma, Qdrant), semantic search, retrieval pipelines, LLM agent tool-calling.
  • Backend Engineering: FastAPI endpoints, asynchronous processing, PostgreSQL persistence.
  • MLOps & Shipping: Containerization with Docker, CI/CD pipelines, latency benchmarking, and model evaluation.

Engineering Target

The Complete AI Engineer Standard

Moving beyond shallow scripting to building explainable, robust, and deployable software systems.

01 · UNDERSTAND

Mathematical Clarity

Ability to explain model loss functions, optimization surfaces, and gradient behavior without treating models as black boxes.

02 · ARCHITECT

Full-Stack Integration

Connecting raw data ingestion, preprocessing pipelines, model inference, and frontend interfaces into a cohesive product.

03 · SHIP

Production Reliability

Containerizing workloads with Docker, securing API endpoints, monitoring latency, and establishing rigorous test suites.