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AI & Data

AI & Data Pathway Curriculum

Comprehensive coverage from basic AI concepts to advanced Machine Learning models.

Curriculum Breakdown

1

Basic

AI & Data Learning

  • AI: introducing the basics
  • 1.1 What is AI?
  • 1.2 How AI Learns
  • 1.3 What Can AI Do?
  • 1.4 Hands-on Activities
  • Data & AI
  • 1.5 What is Data for AI?
  • 1.6 What are Algorithms for AI?
  • 1.7 Simple Algorithm Examples
  • 1.8 Writing Simple Algorithms Unplugged
Assessments: Formative: 10
Intermediate

AI Techniques & Technologies

  • 1.9 Understanding AI Techniques.
  • 2.0 Understanding AI Technologies.
  • Project: Build a simple AI data model or any other.
Assessments: Formative: 10
Advanced

Module Assessments

  • Complete advanced learning and summative evaluations for this module.
Assessments: Formative: 10, Summative: 20
2

Basic

Intro to Machine Learning

  • 1.1 What is ML?
  • 1.2 Supervised Learning
  • 1.3 Unsupervised Learning
  • 1.4 Understanding Model Evaluation and Optimization
Assessments: Formative: 10
Intermediate

Deep Learning & Neural Networks

  • 1.5 Neural Network Fundamentals
  • 1.6 Understanding Convolutional Neural Networks (CNNs)
  • 1.7 Recurrent Neural Networks (RNNs)
  • 1.8 Introduction to Frameworks
Assessments: Formative: 10
Advanced

Specialized AI Domains

  • 1.9 NLP Concepts & Key Applications
  • 2.0 Understanding Computer Vision
  • Project 2.1: Build a simple ML data model or any other.
Assessments: Formative: 5, Summative: 20
3

Basic

Data Science

  • 1.1 Introduction to Data Science.
  • 1.2 Data Analysis & Training a Model.
  • 1.3 Bias in AI.
  • 1.3 Hands-on Activities (Collecting data and training models using available tools).
Assessments: Formative: 5
Intermediate

Prompt Engineering

  • 1.4 Core components of a prompt.
  • 1.5 Zero-shot Prompting.
  • 1.6 Few-shot Prompting.
  • 1.7 Chain-of-Thought (COT).
  • 1.8 Hands-on Activities (learning different prompting techniques).
Assessments: Formative: 10
Advanced

AI & Python

  • 1.9 Introduction to the Python language for AI.
  • 2.0 Introduction to Python Libraries for AI.
  • Project 2.1: Students can build and train a simple model for classification or regression.
Assessments: Formative: 5, Summative: 20
4

Basic

AI Domains Fundamentals

  • 1.1 Introduction to NLP.
  • 1.2 Introduction to Computer Vision.
  • 1.3 Introduction to Language Models.
  • 1.4 Hands-on Activities.
Assessments: Formative: 10
Intermediate

Language Models

  • 1.5 How Language Models Work & Transformer Models.
  • 1.6 Understanding Pre-training, Fine-Tuning, and RLHF.
  • 1.8 Hands-on Activities (learning transformer techniques).
  • Project 1.7: Understanding LLMs and their future.
Assessments: Formative: 10
Advanced

Critical Thinking & Applied Python

  • 1.8 Thinking Critically About AI (Challenges and Ethics).
  • 1.9 Understand autonomous systems.
  • Project 2.1: Pick a short paragraph and write a Python script using NLTK/spaCy for Tokenization, Stop Word Removal, Stemming/Lemmatization.
Assessments: Formative: 5, Summative: 20