Module 1. General AI (28 September - 2 October 2026)

[Slide Deck] Technical Introduction to AI

A foundational session introducing the core concepts behind artificial intelligence and machine learning, including how AI systems learn from data, make predictions, and are applied to real-world problems.

→ Complete the Exploring AI with DIMER activity

[Slide Deck] Fields and Applications of AI

An overview of the major fields of artificial intelligence and how AI techniques are applied across different domains, industries, and real-world problems.

[Slide Deck] Recent Trends in AI

A simplified overview of recent developments in artificial intelligence, introducing emerging approaches, capabilities, and trends shaping how AI systems are being used today.

[Slide Deck] The AI Lifecycle

An introduction to the end-to-end AI/ML lifecycle, from defining a problem and preparing data to developing, evaluating, deploying, and maintaining machine learning solutions.

[Slide Deck] Data for AI

An introduction to the role of data in artificial intelligence, covering how data is collected, prepared, organized, and used to train and evaluate AI/ML systems.

[Slide Deck] Classical Machine Learning Pipeline

A practical walkthrough of the classical machine learning pipeline, covering problem definition, data collection and cleaning, feature engineering, data splitting, model selection and training, evaluation, hyperparameter tuning, and deployment and monitoring. 

California Housing Price Prediction 

[Slide Deck] Responsible AI

An introduction to responsible AI principles and practices, focusing on how AI systems should be developed and used with appropriate consideration for fairness, transparency, accountability, privacy, safety, and human oversight.

Introduction to Explainable AI, LIME, and SHAP (Tabular Data) 

Counterfactual Explanations with DiCE 

Explainability for Deep Learning: Grad-CAM for Computer Vision 

Explainability for Deep Learning: Time Series Models