Understanding AI & Machine Learning
A comprehensive, 3-pillar curriculum that builds durable understanding of artificial intelligence and machine learning — from the mathematical foundations and classical algorithms, through modern deep learning architectures, to the societal implications of deploying AI in the real world. Emphasizes timeless principles over transient tools so knowledge remains valuable as the field evolves.
What you'll learn
Foundations of Intelligent Systems
~350hThe mathematical and algorithmic bedrock of AI and machine learning. Covers the math you need (linear algebra, calculus, probability, statistics), classical ML algorithms, optimization, and probabilistic reasoning. These principles are timeless — they haven't changed in decades and won't change in the next decade.
- Mathematical Foundations for Machine Learning(22 concepts)
- What Is Artificial Intelligence(22 concepts)
- Model Evaluation and Selection(22 concepts)
- How Machines Learn(21 concepts)
- Supervised Learning(22 concepts)
- Probabilistic Graphical Models(20 concepts)
- Unsupervised Learning(20 concepts)
- Probability and Statistics for Machine Learning(23 concepts)
- Data Thinking and Preparation(20 concepts)
- Optimization and Search(21 concepts)
Deep Learning & Modern Architectures
~350hNeural networks from first principles through modern architectures — CNNs, transformers, generative models, reinforcement learning, and graph neural networks. Taught as mathematics and architecture rather than framework tutorials, so the knowledge survives tool churn.
- Neural Networks from Scratch(25 concepts)
- Reinforcement Learning(25 concepts)
- Graph and Geometric Machine Learning(23 concepts)
- Sequence Models and Attention(24 concepts)
- Training Deep Networks(24 concepts)
- ML Engineering and Operations(23 concepts)
- Scalable ML Systems(23 concepts)
- Convolutional Neural Networks(20 concepts)
- Generative Models(24 concepts)
- Representation Learning(23 concepts)
AI in the Real World
~300hApplying AI to real domains (NLP, vision, recommender systems) and reasoning about its societal impact — safety, alignment, fairness, governance, human-AI interaction, and economics. These concerns only grow more important as AI capabilities increase.
- AI Governance & Regulation(21 concepts)
- AI Safety & Alignment(23 concepts)
- AI Economics & Labor(20 concepts)
- Computer Vision Applications(23 concepts)
- Fairness, Accountability & Transparency(23 concepts)
- Recommender Systems(22 concepts)
- Human-AI Interaction(21 concepts)
- Frontiers & Open Problems(22 concepts)
- Natural Language Understanding(25 concepts)
- AI for Decision Making(23 concepts)
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