Most AI systems fail because they can’t learn from trial and error. Reinforcement learning changes that by enabling machines to make sequential decisions through adaptive interaction with their environment. This specialization tackles the core challenge of building AI that learns optimal strategies without explicit programming.
Across four integrated courses, you’ll master the complete RL algorithmic landscape. Start with Markov decision processes and value functions, then advance through sample-based methods like Monte Carlo and Sarsa. Progress to function approximation techniques that scale RL to complex problems, culminating in a capstone where you build an end-to-end automated decision system.
What sets this program apart is its focus on foundations over hype. Rather than superficial tool usage, you’ll understand the mathematical principles behind Temporal Difference learning, Q-learning, and Policy Gradients. White and White, renowned researchers at the University of Alberta’s Faculty of Science, guide you through probabilistic AI concepts that underpin modern machine learning breakthroughs.
The practical applications span game AI, recommender systems, smart assistants, supply chain optimization, industrial control, and financial modeling. You’ll work with simulations, implement feature engineering techniques, and master pseudocode translation into working systems. The specialization bridges theory and practice through hands-on programming assignments that build your portfolio.
By completion, you’ll understand how RL complements deep learning, supervised, and unsupervised approaches within the broader ML ecosystem. You’ll know when to apply Dyna for planning, how linear algebra powers value approximation, and why sampling statistics matter for convergence. This foundation prepares you for advanced AI research or immediate application to production systems.
