Analyze how learning systems adapt across sequential tasks without erasing useful prior capabilities.
Examine continual-learning scenarios, stability–plasticity trade-offs, memory and generalization, and a task-agnostic continual reinforcement-learning study. The sequence moves from defining sequential-task evaluation to adaptation mechanisms and memory trade-offs, then considers a simple baseline in a reinforcement-learning setting. Learners distinguish retention from transfer and design evaluations that expose forgetting, task-order effects, and resource costs. Videos have English audio; these independently written notes and assignments do not reproduce transcripts.
Define a continual-learning scenario by its task sequence, access to task identity, data regime, and evaluation protocol. Compare stability–plasticity and memory-based design choices using retention, adaptation, and resource constraints. Design a task-sequence evaluation that measures forgetting, transfer, and sensitivity to task order against a simple baseline.
Prior familiarity with neural networks, optimization, and supervised learning is recommended. Basic probability, train/validation/test evaluation, and comfort reading research methods will help; prior continual-learning implementation is not required.
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Lesson 1 is free
4 lessons · Advanced · Full course.
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