[HIDDEN]
Machine Learning Foundations
Core supervised learning concepts, model families, and evaluation basics.
Not published
Machine Learning
CLASSIFICATION
14 tracks / 292 lessons
TRACKS
[HIDDEN]
Deep Learning and Neural Networks
Neural network training, deep architectures, representation learning, and deployment patterns.
Not published
[HIDDEN]
Multimodal Foundations and Vision-Language Models
Multimodal encoders, contrastive learning, grounding, and vision-language model design.
Not published
[HIDDEN]
World Models and Model-Based Agents
Latent dynamics, predictive state, imagination-based planning, and model-based agent architectures.
Not published
[HIDDEN]
Diffusion, Audio, Video, and Generative Media
Diffusion models, latent media generation, controllability, and evaluation across image, audio, and video systems.
Not published
[DRAFT]
Causal ML and Decision Making
Interventions, counterfactual thinking, uplift, and the use of causal structure to support better decisions than prediction alone.
Not published
[DRAFT]
Optimization, Generalization, and Learning Theory
Bias-variance trade-offs, sample complexity, optimization behavior, and the theory that explains why learning succeeds or fails.
Not published
[DRAFT]
GPU Systems and Accelerators
Draft track for GPU execution models, accelerator runtime behavior, and heterogeneous systems design.
Not published
[DRAFT]
ML Systems and Training Infrastructure
Draft track for feature platforms, training pipelines, experiment systems, model deployment, and inference operations.
Not published
[DRAFT]
Probabilistic Modeling and Bayesian Inference
Latent-variable models, priors, posterior reasoning, and the probabilistic view of learning under uncertainty.
Not published
[DRAFT]
Reinforcement Learning and Sequential Decision Making
Value functions, policy learning, exploration, planning, and the algorithms for acting under delayed feedback.
Not published
[DRAFT]
Representation Learning and Self-Supervision
Embeddings, contrastive objectives, pretext tasks, and the training recipes that build reusable latent structure from raw data.
Not published
[DRAFT]
MLOps, DataOps, and Model Operations
Operational discipline for machine learning: datasets, training pipelines, evaluation, deployment, monitoring, drift, lineage, and rollback.
Not published
[DRAFT]
AI Research Literacy and Paper Reading
Read AI research with discipline: claims, baselines, ablations, datasets, benchmarks, limitations, replication, and implementation judgment.
Not published