Machine Learning Foundations
A decision-centered introduction to supervised learning: frame prediction problems, trace how models learn, compare model biases, evaluate generalization honestly, and diagnose what to improve next.
Machine Learning
TRACKS
Deep Learning and Neural Networks
How neural networks build representations, receive credit through gradients, train reliably, encode architectural bias, transfer knowledge, and cross the boundary into inference systems.
Multimodal Foundations and Vision-Language Models
Multimodal encoders, contrastive learning, grounding, and vision-language model design.
World Models and Model-Based Agents
Latent dynamics, predictive state, imagination-based planning, and model-based agent architectures.
Diffusion, Audio, Video, and Generative Media
Diffusion models, latent media generation, controllability, and evaluation across image, audio, and video systems.
Causal ML and Decision Making
Interventions, counterfactual thinking, uplift, and the use of causal structure to support better decisions than prediction alone.
Optimization, Generalization, and Learning Theory
Bias-variance trade-offs, sample complexity, optimization behavior, and the theory that explains why learning succeeds or fails.
GPU Systems and Accelerators
Draft track for GPU execution models, accelerator runtime behavior, and heterogeneous systems design.
ML Systems and Training Infrastructure
Draft track for feature platforms, training pipelines, experiment systems, model deployment, and inference operations.
Probabilistic Modeling and Bayesian Inference
Latent-variable models, priors, posterior reasoning, and the probabilistic view of learning under uncertainty.
Reinforcement Learning and Sequential Decision Making
Value functions, policy learning, exploration, planning, and the algorithms for acting under delayed feedback.
Representation Learning and Self-Supervision
Embeddings, contrastive objectives, pretext tasks, and the training recipes that build reusable latent structure from raw data.
MLOps, DataOps, and Model Operations
Operational discipline for machine learning: datasets, training pipelines, evaluation, deployment, monitoring, drift, lineage, and rollback.
AI Research Literacy and Paper Reading
Read AI research with discipline: claims, baselines, ablations, datasets, benchmarks, limitations, replication, and implementation judgment.