AI Project

Abstract

The course guides participants in teams through building end-to-end ML systems for real business problems. Covering the full lifecycle from problem formulation to deployment, participants tackle real-world challenges: imperfect data, bias/fairness, regulatory compliance, and performance trade-offs. Hands-on work includes a baseline pipeline plus optional extensions in areas of interest.

Objective

(1) design and implement a complete ML pipeline from problem formulation to API deployment (2) identify, implement and evaluate suitable models for a given task (3) evaluate and mitigate bias, fairness, and regulatory risks (4) make and defend architectural decisions based on real-world constraints

Content

Main topics: problem formulation; data preparation; algorithm selection & feature engineering; hyperparameter optimization; model evaluation & testing; bias, fairness & regulatory compliance; interpretability methods. Additional extensions available: advanced architectures, task-specific fairness metrics, visualizations, API deployment, and documenting experimental failures.

Resources