Learning, inference, and decision-making from perceived data.
Machine learning is one half of the intelligence layer of Neuroide. This topic covers probabilistic inference, optimization, representation learning, uncertainty, robustness, and evaluation as the methods that turn perceived data into internal models and decisions.
Inference
Bayesian approximations, variational methods, latent-variable models, and structured uncertainty.
Representation
Contrastive learning, self-supervision, multimodal embeddings, and geometry of learned spaces.
Reliability
Calibration, robustness, distribution shift, error analysis, and benchmark design.
Optimization
Loss design, regularization, scaling behavior, and the computational logic behind modern training.