The MLD’s MS in Machine Learning combines rigorous theory, algorithmic depth, and real-world deployment. Students tackle convex optimization proofs in the morning and train trillion-parameter language models by night on the Pittsburgh Supercomputing Center. A mandatory Responsible ML course embeds fairness audits into every project, and the practicum places you at AI-first companies or federal research labs pushing the envelope on scientific discovery.
Contrastive self-supervised vision model for few-shot wildlife classification
Causal-inference framework integrating DAG discovery into neural nets
Curriculum RL for multi-agent coordination in traffic simulators
Neural ODEs modeling pandemic spread with sparse mobility data
Privacy-preserving federated gradient aggregation resistant to poisoning
Hyperparameter-free optimizer benchmarked on transformers and GNNs
Graph-based recommender mitigating filter-bubble effects
Sparse mixture-of-experts scaling study on commodity GPUs
Active-learning tool reducing labeling cost in medical-image annotation
Robust adversarial defense using randomized smoothing for vision models
Temporal-fusion decoder for multivariate renewable-energy forecasting
Fairness-aware credit-risk model with monotonic neural networks
Domain-adaptation pipeline for speech recognition across accents
Meta-learning benchmark evaluating rapid adaptation under covariate shift
Ethics assessment on environmental cost of large-scale model training
Master theory and large-scale practice in CMU’s original ML program.
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