About This Course

Machine Learning for Smarter Innovation is a BSc-level course that bridges Machine Learning and AI with Design Thinking methodologies.

Course Philosophy

This course demonstrates how machine learning augments human-centered design processes through practical applications and real algorithm implementations.

The Innovation Diamond

Our core concept - the Innovation Diamond - visualizes the innovation process:

Divergent Phase: From 1 challenge to 5000 possibilities through creative exploration
Convergent Phase: ML-powered filtering to identify the 5 most strategic solutions

Learning Objectives

By completing this course, students will:

Understand fundamental ML algorithms and their applications
Apply ML techniques to real-world innovation challenges
Integrate data-driven insights with design thinking stages
Evaluate and validate ML models appropriately
Consider ethical implications of AI systems

Course Structure

The course is organized into 14 interconnected topics, progressing from foundations through advanced applications:

Foundations: ML basics, supervised/unsupervised learning, neural networks
Core Techniques: Clustering, classification, NLP, topic modeling
Advanced Applications: Generative AI, structured output, validation
Specialized: Responsible AI, finance applications

Prerequisites

Basic programming knowledge (Python recommended)
Statistics fundamentals
Curiosity about machine learning applications

Instructors

This course is developed and maintained by the Digital AI Finance group.

Repository

All course materials are available on GitHub.