Próximamente
Artificial Intelligence & Machine Learning
Sé el ingeniero que construye IA, no solo la usa.
El curso completo
- módulos
- 98
- temas
- 1118
- min por lección
- 15
Lo que sabrás hacer.
Convierte cualquier problema real en una tarea de IA
Etapa 2 · Search, Logic & Reasoning
Entrena, ajusta y evalúa modelos clásicos
Etapa 3 · Machine Learning Core
Construye y depura redes neuronales profundas
Etapa 4 · Deep & Reinforcement Learning
Ajusta y alinea modelos de lenguaje
Etapa 5 · Generative AI & Agents
Lanza pipelines RAG y agentes que usan herramientas
Etapa 5 · Generative AI & Agents
Despliega, monitoriza y protege IA en producción
Etapa 6 · Production, Trust & Research
Siete etapas.Una sola subida constante.
Cada etapa se apoya en la anterior — de las matemáticas de cada modelo a tu propio proyecto final.
98módulos
en 7 etapas
1118temas
≈ 11 por módulo
≈280horas
en lecciones de 15 minutos
≈3años
≈18meses
≈9meses
para terminar el curso completo
- 1
Foundations & Math
Las matemáticas detrás de cada modelo
7 módulos · 77 temas · ≈ 19 h
- 2
Search, Logic & Reasoning
Cómo buscan y razonan las máquinas
9 módulos · 100 temas · ≈ 25 h
- 3
Machine Learning Core
Modelos que aprenden de los datos
21 módulos · 233 temas · ≈ 58 h
- 4
Deep & Reinforcement Learning
Redes que ven, leen y actúan
24 módulos · 276 temas · ≈ 69 h
- 5
Generative AI & Agents
LLM, RAG y agentes
18 módulos · 211 temas · ≈ 53 h
- 6
Production, Trust & Research
IA que aguanta en el mundo real
15 módulos · 178 temas · ≈ 45 h
- 7
Practicum & Capstone
Tu propio sistema, de principio a fin
4 módulos · 43 temas · ≈ 11 h
Meses
01224048120246
MesesMeta · ≈ 3 años · ≈ 18 meses · ≈ 9 meses
Las horas y los meses son estimaciones: una lección de 15 minutos por tema, cada día.
Cada módulo.Cada tema.
Los títulos de módulos y temas se quedan en inglés, el idioma de trabajo del sector.
Etapa 1
Foundations & Math
Las matemáticas detrás de cada modelo
7 módulos · 77 temas
1The Landscape of Artificial Intelligence10 temas
- Artificial Intelligence, Machine Learning, and Deep Learning
- Symbolic, Statistical, and Neural Approaches
- Prediction, Generation, Reasoning, and Decision-Making
- Specialized and General-Purpose Models
- Learning Systems and Programmed Rules
- Capabilities, Limitations, and Evaluation Evidence
- Historical Shifts in AI Methods
- Data, Compute, and Algorithmic Progress
- Research Models and Operational AI Systems
- Boundaries Between AI Engineering and Data Analytics
2Intelligent Agents and Problem Formulation11 temas
- Agents, Environments, Observations, and Actions
- Goals, Rewards, Utilities, and Constraints
- Fully and Partially Observable Environments
- Deterministic and Stochastic Environments
- Episodic and Sequential Tasks
- Single-Agent and Multi-Agent Settings
- Reactive, Deliberative, and Learning Agents
- Translating User Needs Into AI Tasks
- Choosing Prediction and Decision Boundaries
- Defining Success, Failure, and Resource Budgets
- Establishing Baselines Before Selecting Models
3Linear Algebra for Learning Systems11 temas
- Vectors, Matrices, and Higher-Order Tensors
- Matrix Products and Batched Operations
- Norms, Distances, and Similarity Measures
- Linear Independence, Rank, and Null Spaces
- Projections and Least-Squares Geometry
- Eigenvalues and Eigenvectors in Learning
- Singular Value Decomposition
- Positive Semidefinite Matrices
- Low-Rank Approximations and Embedding Spaces
- Conditioning and Numerical Sensitivity
- Tensor Shapes and Broadcasting Rules
4Calculus and Automatic Differentiation11 temas
- Derivatives of Scalar and Vector Functions
- Gradients, Jacobians, and Hessians
- Multivariable Chain Rules
- Directional Derivatives and Local Approximations
- Matrix Calculus for Model Parameters
- Computational Graphs and Differentiable Operations
- Forward-Mode and Reverse-Mode Differentiation
- Jacobian-Vector and Vector-Jacobian Products
- Nondifferentiable Points and Subgradients
- Gradient Checking With Numerical Differences
- Differentiation Through Iterative Computations
5Probability and Information for Machine Learning11 temas
- Random Variables and Probability Distributions
- Joint, Conditional, and Marginal Probabilities
- Independence and Conditional Independence
- Bayes' Rule in Model Learning
- Expectations, Variance, and Covariance
- Likelihoods and Log-Likelihoods
- Entropy and Cross-Entropy
- Kullback-Leibler Divergence
- Mutual Information and Representation Quality
- Sampling and Monte Carlo Estimates
- Numerical Stability in Probability Calculations
6Optimization and Numerical Methods12 temas
- Objective Functions and Feasible Solutions
- Convex and Nonconvex Optimization
- First-Order and Second-Order Information
- Gradient Descent and Step-Size Selection
- Stochastic Gradients and Mini-Batches
- Constraints, Penalties, and Lagrange Multipliers
- Coordinate Descent and Proximal Methods
- Local Minima, Saddle Points, and Flat Regions
- Convergence Criteria and Optimization Diagnostics
- Floating-Point Precision and Numerical Errors
- Stable Logarithms, Exponentials, and Normalization
- Optimization Error and Modeling Error
7Computing Workflows for AI Experiments11 temas
- Array-Based and Tensor-Based Computation
- Vectorization and Batch Processing
- CPU and Accelerator Execution
- Tensor Devices, Layouts, and Data Types
- Dataset Iterators and Mini-Batch Construction
- Reproducible Randomness and Seed Management
- Experiment Configuration and Artifact Organization
- Model Checkpoints and Training State
- Profiling Runtime and Memory Use
- Numerical Reference Implementations
- Framework Abstractions and Their Tradeoffs
Etapa 2
Search, Logic & Reasoning
Cómo buscan y razonan las máquinas
9 módulos · 100 temas
8State-Space Search and Problem Solving12 temas
- State Representations and Transition Models
- Initial States, Goals, and Path Costs
- Search Trees and State Graphs
- Breadth-First and Depth-First Strategies
- Uniform-Cost and Iterative Deepening Search
- Heuristic Estimates and Search Guidance
- A-Star Search and Heuristic Conditions
- Duplicate States and Cycle Handling
- Memory-Bounded and Anytime Search
- Search Under Partial Information
- Completeness, Optimality, and Computational Cost
- Constructing Heuristics From Problem Structure
9Local, Stochastic, and Evolutionary Search11 temas
- Optimization Through Neighboring Solutions
- Hill Climbing and Random Restarts
- Simulated Annealing
- Tabu Search and Search Memory
- Beam Search and Population-Based Search
- Genetic Representations and Variation Operators
- Selection Pressure and Population Diversity
- Evolution Strategies for Black-Box Objectives
- Multiobjective Search and Pareto Fronts
- Constraint Handling in Stochastic Search
- Comparing Search Methods Under Equal Budgets
10Constraint-Based Reasoning11 temas
- Variables, Domains, and Constraints
- Constraint Graphs and Problem Structure
- Backtracking and Variable Ordering
- Constraint Propagation and Consistency
- Global Constraints and Domain Reduction
- Boolean Satisfiability Concepts
- Satisfiability Modulo Theories
- Constraint Optimization and Soft Preferences
- Scheduling and Allocation as Constraint Problems
- Integrating Learned Heuristics With Solvers
- Explaining Infeasible or Conflicting Requirements
11Logic and Automated Inference11 temas
- Propositional and First-Order Representations
- Facts, Rules, Relations, and Quantifiers
- Syntax, Semantics, and Entailment
- Rule-Based Knowledge Systems
- Unification and Substitution
- Forward and Backward Reasoning
- Resolution and Proof Search
- Soundness and Completeness of Inference
- Default Assumptions and Nonmonotonic Reasoning
- Inconsistent Knowledge and Belief Revision
- Limits of Purely Symbolic Inference
12Knowledge Representation and Knowledge Graphs11 temas
- Entities, Relations, and Attributes
- Taxonomies, Ontologies, and Type Systems
- Events, Time, and Context in Knowledge Models
- Knowledge Graph Construction and Curation
- Entity Resolution and Relation Linking
- Schema Constraints and Knowledge Validation
- Graph Queries and Rule-Based Enrichment
- Provenance and Confidence of Knowledge Claims
- Knowledge Graph Embeddings
- Link Prediction and Graph Completion
- Combining Structured Knowledge With Learned Models
13Automated Planning11 temas
- Actions, Preconditions, and Effects
- Planning States and Goal Conditions
- Forward and Backward Planning
- Partial-Order and Hierarchical Plans
- Planning With Time and Resources
- Planning Under Uncertain Outcomes
- Conditional Plans and Belief States
- Replanning After Observed Changes
- Learned Models and Planning Heuristics
- Plan Verification and Execution Monitoring
- Planning Quality and Computational Tradeoffs
14Game Playing and Strategic Interaction11 temas
- Game States, Actions, and Payoffs
- Minimax Reasoning and Evaluation Functions
- Alpha-Beta Search and Move Ordering
- Stochastic Game Trees
- Monte Carlo Tree Search
- Imperfect Information and Hidden State
- Cooperative and Competitive Objectives
- Best Responses and Equilibrium Concepts
- Self-Play and Opponent Diversity
- Exploitability and Robust Strategy Evaluation
- Combining Search With Learned Policies and Values
15Probabilistic Graphical Models11 temas
- Graph Structure and Conditional Dependence
- Bayesian Networks and Factorization
- Markov Random Fields and Undirected Models
- Factor Graphs and Local Potentials
- Hidden Variables and Structured Uncertainty
- Hidden Markov Models
- Dynamic Bayesian Networks
- Conditional Random Fields
- Learning Graph Parameters and Structure
- Modeling Missing and Partially Observed Variables
- Choosing Graphical Models for Structured Problems
16Probabilistic Inference Methods11 temas
- Exact Marginalization and Variable Elimination
- Message Passing and Belief Propagation
- Inference Complexity and Graph Structure
- Maximum A Posteriori Inference
- Importance Sampling and Weight Degeneracy
- Markov Chain Monte Carlo Concepts
- Gibbs and Metropolis-Hastings Sampling
- Sequential Monte Carlo and Particle Methods
- Variational Approximations
- Evidence Lower Bounds and Optimization
- Diagnosing Approximate Inference Quality
Etapa 3
Machine Learning Core
Modelos que aprenden de los datos
21 módulos · 233 temas
17Learning Paradigms and Objective Design11 temas
- Supervised and Unsupervised Learning
- Self-Supervised and Reinforcement Learning
- Parametric and Nonparametric Models
- Discriminative and Generative Learning
- Batch, Online, and Incremental Learning
- Empirical Risk Minimization
- Loss Functions and Task Alignment
- Regularization as an Inductive Preference
- Point Predictions and Predictive Distributions
- Multiobjective and Cost-Sensitive Learning
- Matching Learning Paradigms to Available Feedback
18Training Data and Annotation12 temas
- Defining the Target Population and Unit of Prediction
- Data Provenance and Permitted Use
- Label Definitions and Annotation Guidelines
- Human Annotation and Agreement Analysis
- Label Noise and Ambiguous Examples
- Class Balance and Coverage of Rare Cases
- Duplicate and Near-Duplicate Examples
- Training Data Contamination
- Dataset Versioning and Documentation
- Synthetic Data and Its Limitations
- Data Quality Improvements as Model Interventions
- Dataset Growth and Collection Priorities
19Features and Model Input Representations12 temas
- Numerical and Categorical Feature Representations
- Scaling, Normalization, and Model Sensitivity
- Missing Values and Missingness Indicators
- Category Encoding and High-Cardinality Inputs
- Feature Interactions and Basis Expansions
- Sparse and Dense Representations
- Learned Embeddings for Discrete Inputs
- Feature Selection and Redundant Inputs
- Preprocessing Fit Boundaries
- Temporal Availability of Features
- Feature Leakage and Proxy Information
- Reproducible Model Input Pipelines
20Training, Validation, and Generalization Design11 temas
- Training, Validation, and Test Roles
- Random, Stratified, and Grouped Splits
- Time-Aware and Forward Evaluation
- Cross-Validation and Nested Selection
- Leakage Across Related Examples
- Distribution Differences Between Development and Use
- Validation Set Reuse and Adaptive Overfitting
- Learning Curves and Data Requirements
- Baseline Comparisons and Ablation Studies
- Uncertainty Across Data Splits and Random Seeds
- Holding Out Realistic Failure Scenarios
21Learning Theory and Inductive Bias11 temas
- Hypothesis Classes and Model Capacity
- Bias, Variance, and Approximation Error
- Generalization Error and Expected Risk
- Overfitting and Underfitting Mechanisms
- Sample Complexity and PAC Learning Concepts
- VC Dimension and Complexity Measures
- Regularization and Stability Connections
- No-Free-Lunch Results and Their Interpretation
- Interpolation and Double Descent
- Implicit Bias of Optimization
- Limits of Theory in Modern Deep Learning
22Predictive Model Evaluation12 temas
- Regression Error and Scale Sensitivity
- Classification Confusion Patterns
- Precision, Recall, and Class-Specific Tradeoffs
- ROC and Precision-Recall Curves
- Threshold Selection Under Unequal Costs
- Log Loss and Proper Scoring Rules
- Probability Calibration and Reliability Diagrams
- Macro, Micro, and Weighted Aggregation
- Multilabel and Structured Prediction Metrics
- Subgroup and Slice-Based Error Analysis
- Abstention and Selective Prediction
- Matching Evaluation Metrics to Deployment Decisions
23Model Selection and Hyperparameter Optimization11 temas
- Hyperparameters and Learned Parameters
- Grid, Random, and Adaptive Search
- Bayesian Optimization for Model Selection
- Early-Stopping and Resource Allocation Strategies
- Successive Halving and Multi-Fidelity Evaluation
- Conditional and Structured Search Spaces
- Search Budget and Fair Model Comparisons
- Repeated Trials and Ranking Uncertainty
- Tracking Experiments and Model Lineage
- Selecting Models Under Latency and Memory Constraints
- Final Evaluation After Model Selection
24Linear Predictors and Regularized Regression11 temas
- Linear Prediction as a Learning Model
- Squared, Absolute, and Robust Regression Losses
- Closed-Form and Iterative Parameter Estimation
- Polynomial and Basis-Function Models
- Ridge, Lasso, and Elastic Net Penalties
- Sparse Coefficients and Correlated Features
- Multioutput Linear Prediction
- Quantile Regression Objectives
- Online Updates for Linear Models
- Optimization and Scaling Considerations
- Linear Models as Diagnostic Baselines
25Probabilistic Classification Models11 temas
- Binary Logistic Prediction
- Multiclass Softmax Models
- Decision Boundaries and Class Probabilities
- Cross-Entropy Training Objectives
- Regularized Classification
- Naive Bayes Model Families
- Gaussian Discriminant Models
- Generative and Discriminative Classifiers
- Class Weighting and Prior Probability Changes
- Multilabel Classification Strategies
- Calibrating and Comparing Simple Classifiers
26Instance-Based and Distance-Based Learning10 temas
- Nearest-Neighbor Classification and Regression
- Distance Functions and Feature Scaling
- Neighborhood Size and Local Smoothing
- Distance-Weighted Predictions
- High-Dimensional Geometry and Distance Concentration
- Prototype and Centroid-Based Models
- Locally Weighted Regression
- Learned Distance Metrics
- Search Cost and Approximate Neighbor Retrieval
- Memory Requirements and Incremental Updates
27Kernel Methods and Support Vector Machines11 temas
- Feature Maps and Kernel Similarity
- Positive Semidefinite Kernel Functions
- Maximum-Margin Classification
- Hard and Soft Margin Objectives
- Support Vectors and Decision Functions
- Dual Optimization and Kernelization
- Linear, Polynomial, and Radial Basis Kernels
- Support Vector Regression
- Kernel Hyperparameters and Scaling
- Kernel Approximation for Larger Datasets
- Computational Limits of Kernel Models
28Decision Trees and Rule Learning10 temas
- Recursive Partitioning of Feature Space
- Impurity and Information-Based Split Criteria
- Regression Tree Objectives
- Categorical and Missing-Value Handling
- Tree Depth and Minimum Leaf Constraints
- Pruning and Complexity Control
- Instability of Individual Trees
- Rule Extraction and Decision Paths
- Monotonicity and Domain Constraints
- Interpreting Tree Predictions and Failure Regions
29Ensemble Learning and Boosting12 temas
- Diversity and Error Reduction in Ensembles
- Bootstrap Aggregation
- Random Forest Construction
- Randomized Tree Ensembles
- Adaptive Boosting and Reweighted Examples
- Gradient Boosting as Functional Optimization
- Second-Order Boosting Objectives
- Shrinkage, Subsampling, and Early Stopping
- Histogram-Based and Categorical Boosting Techniques
- Stacking and Out-of-Fold Predictions
- Voting and Probability Averaging
- Ensemble Cost, Calibration, and Interpretability
30Bayesian Learning and Gaussian Processes11 temas
- Priors, Likelihoods, and Posterior Learning
- Bayesian Linear Prediction
- Posterior Predictive Distributions
- Model Evidence and Complexity Preferences
- Gaussian Processes as Distributions Over Functions
- Covariance Functions and Kernel Design
- Gaussian Process Regression
- Gaussian Process Classification Concepts
- Hyperparameter Learning Through Marginal Likelihood
- Sparse Approximations and Inducing Variables
- Uncertainty Quality Under Model Misspecification
31Clustering Algorithms11 temas
- Similarity-Based Grouping and Cluster Assumptions
- K-Means and Centroid Optimization
- Initialization and Cluster Stability
- Hierarchical and Agglomerative Clustering
- Density-Based Cluster Discovery
- Spectral Clustering and Graph Structure
- Soft Assignments and Overlapping Groups
- Constraints and Prior Knowledge in Clustering
- Internal and External Cluster Evaluation
- High-Dimensional and Unequal-Density Challenges
- Interpreting Clusters Without Inventing Categories
32Dimensionality Reduction and Latent Structure11 temas
- Linear Projection and Variance Preservation
- Principal Component Analysis Algorithms
- Truncated SVD for Sparse Inputs
- Independent Component Analysis
- Nonnegative Matrix Factorization
- Random Projections and Distance Preservation
- Manifold Learning and Neighborhood Structure
- Nonlinear Embedding Methods
- Reconstruction, Compression, and Predictive Utility
- Visualization Distortion and Interpretation Limits
- Evaluating Reduced Representations Downstream
33Mixture Models and Density Estimation11 temas
- Latent Components and Mixture Distributions
- Gaussian Mixture Models
- Expectation-Maximization Updates
- Initialization and Local Optima in EM
- Component Number and Model Selection
- Covariance Constraints and Singular Solutions
- Kernel Density Estimation
- Conditional Density Models
- Missing Data Through Latent Variable Models
- Density Estimation in High Dimensions
- Likelihood Quality and Task Usefulness
34Anomaly and Novelty Detection11 temas
- Point, Contextual, and Collective Anomalies
- Novelty Detection and Out-of-Distribution Inputs
- Distance and Local Density Methods
- Isolation-Based Models
- One-Class Classification
- Reconstruction-Based Anomaly Scores
- Sequence and Context-Aware Detection
- Rare Positive Labels and Contaminated Training Sets
- Thresholds Under False Alarm Constraints
- Evaluation Under Extreme Class Imbalance
- Analyst Feedback and Changing Definitions of Normality
35Semi-Supervised and Weakly Supervised Learning11 temas
- Learning From Labeled and Unlabeled Examples
- Assumptions Behind Semi-Supervised Learning
- Pseudo-Labels and Confidence Thresholds
- Consistency-Based Training
- Graph-Based Label Propagation
- Multi-View and Co-Training Concepts
- Learning From Noisy Labeling Rules
- Label Model Conflicts and Correlations
- Positive-Unlabeled Learning
- Multiple-Instance Learning
- Evaluating Gains Against Labeling Effort
36Active Learning and Human Feedback11 temas
- Pool-Based and Stream-Based Querying
- Uncertainty Sampling
- Diversity and Coverage in Acquisition
- Query-by-Committee Methods
- Batch Selection and Annotation Efficiency
- Expected Improvement and Value of Information
- Labeling Cost and Annotator Expertise
- Human Review of Ambiguous Examples
- Feedback Loops and Selection Bias
- Stopping Criteria for Data Acquisition
- Comparing Active Learning With Random Sampling
37Transfer, Multitask, and Domain Adaptation11 temas
- Source and Target Learning Problems
- Reusing Features and Learned Representations
- Shared Backbones and Task-Specific Heads
- Task Balancing and Gradient Interference
- Negative Transfer and Task Compatibility
- Covariate and Label Distribution Shifts
- Domain-Invariant Representations
- Importance Weighting and Adaptation Assumptions
- Few-Shot and Meta-Learning Concepts
- Domain Generalization Without Target Labels
- Measuring Transfer Benefits and Failure Cases
Etapa 4
Deep & Reinforcement Learning
Redes que ven, leen y actúan
24 módulos · 276 temas
38Neural Network Foundations11 temas
- Artificial Neurons and Weighted Computation
- Multilayer Perceptron Architectures
- Activation Functions and Their Effects
- Hidden Representations and Feature Composition
- Output Layers for Different Learning Tasks
- Parameter Sharing and Architectural Bias
- Depth, Width, and Expressive Capacity
- Residual Connections and Information Flow
- Initialization and Signal Propagation
- Neural Networks as Function Approximators
- Architectural Choices Under Resource Constraints
39Backpropagation and Training Implementation11 temas
- Forward Passes and Loss Computation
- Backpropagation Through Layered Computation
- Parameter Gradients and Update Steps
- Automatic Differentiation in Neural Frameworks
- Implementing Neural Models With PyTorch
- Gradient Accumulation and Reset Behavior
- Training and Evaluation Modes
- Detaching Computations and Freezing Parameters
- Custom Losses and Differentiable Components
- Checkpointing Model and Optimizer State
- Verifying a Training Loop on Small Examples
40Optimization for Deep Networks12 temas
- Stochastic Gradient Descent With Momentum
- Adaptive Gradient Methods
- Adam and Decoupled Weight Decay
- Learning Rate Schedules and Warmup
- Batch Size and Gradient Noise
- Gradient Clipping and Exploding Updates
- Mixed-Precision Optimization
- Loss Scaling and Numerical Overflow
- Gradient Accumulation for Large Effective Batches
- Optimizer State and Memory Requirements
- Diagnosing Optimization Instability
- Relating Training Speed to Final Model Quality
41Regularization and Deep Generalization12 temas
- Weight Penalties and Parameter Constraints
- Dropout and Stochastic Computation
- Early Stopping and Checkpoint Selection
- Data Augmentation and Invariance Assumptions
- Label Smoothing
- Mixup and Interpolated Training Examples
- Batch Normalization and Layer Normalization
- Normalization Effects on Optimization and Generalization
- Representation Bottlenecks
- Sharpness, Robustness, and Generalization Hypotheses
- Overparameterization and Memorization
- Separating Regularization Benefits Through Ablations
42Neural Network Debugging12 temas
- Testing Data and Labels Before Training
- Overfitting a Small Batch as a Diagnostic
- Inspecting Activations and Gradient Distributions
- Detecting Dead Units and Saturation
- Diagnosing Vanishing and Exploding Gradients
- Tracking Loss Components and Scale Mismatches
- Identifying NaNs and Numerical Instability
- Finding Train-Evaluation Behavior Differences
- Detecting Incorrect Masking and Padding
- Checking Data Pipeline and Device Bottlenecks
- Structured Ablations for Model Failures
- Building Minimal Reproducible Training Experiments
43Convolutional Neural Networks11 temas
- Convolution as a Learned Local Operator
- Filters, Channels, Stride, and Padding
- Receptive Fields and Spatial Resolution
- Pooling and Downsampling
- Translation Equivariance and Its Limits
- Residual Convolutional Architectures
- Depthwise and Grouped Convolutions
- Dilated Convolutions and Context Range
- Encoder-Decoder Feature Hierarchies
- Pretrained Visual Backbones
- Convolutional Model Efficiency and Scaling
44Learning for Visual Recognition12 temas
- Image Classification and Multilabel Recognition
- Object Detection Formulations
- Localization Losses and Bounding Box Predictions
- Semantic and Instance Segmentation
- Keypoint and Pose Prediction Objectives
- Vision Transformers and Patch Representations
- Dense Prediction and Multiscale Features
- Imbalanced Visual Labels and Small Objects
- Visual Dataset Bias and Shortcut Learning
- Task-Specific Visual Evaluation Metrics
- Transfer Learning Across Visual Domains
- Boundaries Between Model Learning and Vision System Engineering
45Recurrent and Sequential Neural Models11 temas
- Sequence Inputs and Hidden States
- Recurrent Neural Network Computation
- Backpropagation Through Time
- Long Short-Term Memory Networks
- Gated Recurrent Units
- Bidirectional Sequence Encoders
- Sequence-to-Sequence Architectures
- Teacher Forcing and Exposure Bias
- Variable-Length Sequences and Masking
- Temporal Convolutional Alternatives
- Long-Range Dependencies and Memory Limits
46Attention and Transformer Architectures12 temas
- Query, Key, and Value Representations
- Scaled Dot-Product Attention
- Self-Attention and Cross-Attention
- Multihead Attention
- Causal and Padding Masks
- Positional Embeddings and Rotary Position Methods
- Transformer Feedforward Blocks
- Residual Paths and Normalization Placement
- Encoder, Decoder, and Encoder-Decoder Models
- Attention Complexity and Context Length
- Training Objectives for Transformer Families
- Diagnosing Attention and Position Handling Errors
47Efficient Sequence Models and Sparse Architectures11 temas
- Sparse and Local Attention Patterns
- Linear Attention Approximations
- Recurrent Computation in Sequence Models
- State-Space Sequence Modeling
- Selective State Updates
- Hybrid Attention and State-Space Architectures
- Mixture-of-Experts Layers
- Expert Routing and Load Balancing
- Active Parameters and Total Model Capacity
- Context Length and Memory Tradeoffs
- Comparing Architectures Under Matched Compute Budgets
48Graph and Geometric Learning12 temas
- Node, Edge, and Graph Prediction Tasks
- Message Passing and Neighborhood Aggregation
- Graph Convolution and Graph Attention
- Graph-Level Pooling and Readout
- Transductive and Inductive Graph Learning
- Heterogeneous and Temporal Graphs
- Oversmoothing and Oversquashing
- Graph Sampling and Scalability
- Permutation Invariance and Equivariance
- Geometric Symmetries in Learned Models
- Point Sets and Spatially Structured Inputs
- Graph Evaluation Splits and Relational Leakage
49Self-Supervised and Metric Learning11 temas
- Constructing Supervision From Unlabeled Data
- Positive Pairs, Negative Pairs, and Augmentations
- Contrastive Learning Objectives
- Triplet and Ranking-Based Metric Losses
- Negative Sampling and Batch Composition
- Masked Input Reconstruction
- Teacher-Student and Self-Distillation Methods
- Representation Collapse and Prevention Mechanisms
- Embedding Geometry and Transfer Quality
- Linear Probes and Fine-Tuning Evaluation
- Choosing Self-Supervised Tasks for a Data Domain
50Text Representations and Tokenization11 temas
- Text Normalization and Language Variation
- Word, Subword, Byte, and Character Representations
- Byte-Pair and Unigram Tokenization
- Vocabulary Size and Sequence Length Tradeoffs
- Tokenization of Multilingual and Technical Text
- Special Tokens and Conversation Formatting
- Sparse Text Features and Classical Baselines
- Static and Contextual Word Embeddings
- Sentence and Document Representations
- Tokenization Errors and Lost Information
- Tokenizer Consistency Across Training and Inference
51Natural Language Understanding Tasks11 temas
- Text and Document Classification
- Named Entity and Relation Extraction
- Sequence Labeling and Span Prediction
- Semantic Similarity and Paraphrase Detection
- Natural Language Inference
- Extractive Question Answering
- Semantic Parsing and Structured Meaning
- Summarization and Information Preservation
- Translation and Cross-Lingual Transfer
- Ambiguity, Pragmatics, and Context Dependence
- Evaluating Language Tasks Beyond Surface Matching
52Speech and Audio Learning11 temas
- Waveforms, Spectrograms, and Acoustic Features
- Audio Encoders and Temporal Resolution
- Speech Recognition Learning Objectives
- Connectionist Temporal Classification
- Transducer and Encoder-Decoder Speech Models
- Speaker Representations and Diarization
- Audio Event Classification and Detection
- Self-Supervised Speech Representations
- Speech Synthesis and Neural Vocoders
- Noise, Accents, and Domain Variation
- Audio Quality, Intelligibility, and Task Evaluation
53Multimodal Representation Learning12 temas
- Aligned and Unaligned Modalities
- Early, Late, and Intermediate Fusion
- Shared Embedding Spaces Across Modalities
- Image-Text Contrastive Learning
- Cross-Modal Attention and Conditioning
- Vision-Language Understanding
- Video and Temporal Multimodal Inputs
- Audio-Visual Learning
- Missing and Conflicting Modalities
- Grounding Predictions in Input Evidence
- Multimodal Dataset Alignment and Bias
- Evaluating Cross-Modal Transfer and Robustness
54Recommendation, Matching, and Learning to Rank12 temas
- User, Item, and Context Representations
- Collaborative Filtering and Matrix Factorization
- Implicit Feedback and Negative Sampling
- Two-Tower Retrieval Models
- Pointwise, Pairwise, and Listwise Ranking
- Neural Recommendation Architectures
- Sequential Recommendation
- Cold-Start and Sparse-Interaction Problems
- Exposure Bias and Feedback Loops
- Diversity, Coverage, and Relevance Tradeoffs
- Offline Ranking Evaluation and Its Limits
- Retrieval and Ranking as Separate Learning Stages
55Learning From Temporal and Event Data11 temas
- Temporal Prediction Task Definitions
- Lagged Inputs and Sequence Windows
- Global Models Across Multiple Time Series
- Recurrent, Convolutional, and Attention-Based Forecasting
- Multistep Prediction and Error Accumulation
- Probabilistic Forecasts and Quantile Objectives
- Irregular Sampling and Missing Observations
- Event Sequences and Learned Intensity Models
- Covariate Availability at Prediction Time
- Temporal Distribution Shift and Retraining
- Backtesting Learned Models Without Future Leakage
56Bandits and Sequential Experimentation11 temas
- Exploration and Exploitation
- Stochastic Multi-Armed Bandits
- Regret and Sample Efficiency
- Epsilon-Greedy and Confidence-Based Strategies
- Thompson Sampling
- Contextual Bandits
- Delayed and Partial Feedback
- Logged Policies and Propensity Information
- Off-Policy Evaluation for Bandit Decisions
- Nonstationary Rewards and Adaptation
- Constraints on Online Exploration
57Reinforcement Learning Foundations12 temas
- States, Actions, Rewards, and Transitions
- Markov Decision Processes
- Policies and Trajectory Distributions
- Returns, Discounting, and Episode Boundaries
- State-Value and Action-Value Functions
- Bellman Relationships
- Policy Evaluation and Policy Improvement
- Value Iteration and Policy Iteration
- Partial Observability and Belief-State Policies
- Reward Design and Specification Errors
- Environment Design and Evaluation Protocols
- Learning Performance and Sample Efficiency
58Value-Based Reinforcement Learning12 temas
- Monte Carlo Return Estimation
- Temporal-Difference Learning
- SARSA and Q-Learning
- On-Policy and Off-Policy Updates
- Function Approximation for Value Estimates
- Deep Q-Networks
- Replay Buffers and Target Networks
- Double Estimation and Overestimation Bias
- Prioritized Replay and Sampling Effects
- Distributional Value Learning
- Exploration Strategies for Sparse Rewards
- Stability and Reproducibility in Value Learning
59Policy Gradients and Actor-Critic Methods12 temas
- Parameterized Stochastic Policies
- Policy Gradient Estimation
- REINFORCE and Variance Reduction
- Baselines and Advantage Estimates
- Actor-Critic Learning
- Generalized Advantage Estimation
- Trust Regions and Clipped Policy Objectives
- Proximal Policy Optimization
- Deterministic Policy Gradients
- Entropy-Regularized Learning and Soft Actor-Critic
- Continuous Action Constraints
- Policy Evaluation Across Seeds and Environments
60Model-Based and Hierarchical Reinforcement Learning12 temas
- Learning Transition and Reward Models
- Planning With Learned Dynamics
- Model Predictive Decision-Making
- World Models and Latent Rollouts
- Compounding Model Error
- Uncertainty-Aware Planning
- Model-Based and Model-Free Hybrid Methods
- Temporal Abstraction and Options
- Hierarchical Policies and Subgoals
- Goal-Conditioned Learning
- Simulation-to-Environment Transfer
- Evaluating Learned Models Through Decision Quality
61Offline Learning, Imitation, and Demonstrations11 temas
- Learning Policies From Fixed Datasets
- Dataset Coverage and Action Support
- Distribution Shift in Offline Policy Learning
- Conservative Value and Policy Objectives
- Offline Policy Evaluation
- Behavioral Cloning
- Interactive Imitation and Data Aggregation
- Inverse Reinforcement Learning Concepts
- Learning Rewards From Demonstrations
- Demonstration Quality and Conflicting Behavior
- Combining Offline Training With Constrained Online Updates
Etapa 5
Generative AI & Agents
LLM, RAG y agentes
18 módulos · 211 temas
62Generative Modeling and Variational Autoencoders11 temas
- Modeling Data Distributions and Conditional Generation
- Likelihood-Based and Implicit Generative Models
- Autoregressive Factorization Across Data Types
- Latent Variables and Learned Representations
- Deterministic Autoencoders and Reconstruction
- Variational Autoencoder Objectives
- Reparameterization and Stochastic Gradients
- Posterior Collapse and Latent Variable Use
- Conditional and Hierarchical Latent Models
- Discrete Latents and Vector Quantization
- Sample Quality, Coverage, and Likelihood Tradeoffs
63Generative Adversarial Networks10 temas
- Generator and Discriminator Objectives
- Adversarial Training as a Coupled Game
- Conditional Adversarial Generation
- Mode Collapse and Missing Diversity
- Gradient Behavior and Training Instability
- Wasserstein-Based Objectives
- Gradient Penalties and Spectral Constraints
- Image Translation and Paired Supervision
- Latent Space Manipulation
- Evaluating Adversarial Generative Models
64Normalizing Flows10 temas
- Invertible Transformations of Probability Densities
- Change-of-Variables Likelihoods
- Jacobian Determinants and Tractable Architectures
- Coupling and Autoregressive Flow Layers
- Expressive Transformations and Computational Cost
- Conditional Density Estimation With Flows
- Continuous Normalizing Flows
- Neural Differential Equations and Density Evolution
- Sampling Speed and Density Evaluation Tradeoffs
- Comparing Flow Models With Other Generative Families
65Diffusion Models and Flow Matching14 temas
- Forward Noising and Learned Reverse Processes
- Noise, Data, and Score Prediction
- Noise Schedules and Training Weighting
- Denoising Objectives and Score Matching
- Stochastic and Deterministic Sampling
- Sampler Accuracy and Generation Cost
- Latent-Space Diffusion
- Conditional Generation and Guidance
- Probability Paths and Learned Vector Fields
- Conditional Flow-Matching Objectives
- Rectified Flow Concepts
- Connections and Differences Between Generative Formulations
- Distillation Into Faster Generators
- Evaluating Quality, Diversity, and Controllability
66Image, Video, and Audio Generation11 temas
- Text-Conditioned Visual Generation
- Image Editing and Inpainting Objectives
- Spatial Conditioning and Reference Inputs
- Identity, Style, and Composition Consistency
- Video Generation and Temporal Coherence
- Motion Representations and Long-Horizon Consistency
- Audio and Music Generation Models
- Speech Generation and Speaker Conditioning
- Multimodal Generation Interfaces
- Artifact Detection and Perceptual Evaluation
- Provenance, Consent, and Permitted Media Use
67Foundation Language Model Pretraining12 temas
- Next-Token and Denoising Language Objectives
- Decoder-Only Language Model Construction
- Context Windows and Training Sequences
- Token Embeddings and Output Projections
- Vocabulary and Parameter Sharing
- Training From Scratch and Continued Pretraining
- Pretraining Loss and Downstream Capabilities
- Multilingual and Domain-Specific Language Modeling
- Sequence Packing and Attention Boundaries
- Training Stability and Checkpoint Evaluation
- Base Models and Adapted Models
- Limits of Language Modeling as a Training Objective
68Language Model Data Engineering12 temas
- Corpus Selection and Source Mixtures
- Text Extraction and Document Boundaries
- Language Identification and Quality Filtering
- Exact and Approximate Deduplication
- Benchmark and Evaluation Data Decontamination
- Personal Information and Sensitive Content Handling
- Licensed, Public, and Synthetic Training Sources
- Data Mixture Weighting and Sampling Schedules
- Synthetic Data Verification and Diversity
- Repeated Data and Memorization Risk
- Tokenization Quality Across the Corpus
- Data Ablations and Corpus Documentation
69Scaling Laws and Training Resource Planning11 temas
- Model Size, Dataset Size, and Training Compute
- Empirical Scaling Relationships
- Compute-Optimal Allocation Concepts
- Parameter Count and Active Parameter Count
- Training FLOPs and Resource Accounting
- Data Quality and Effective Dataset Size
- Context Length and Training Cost
- Small-Scale Experiments for Larger Training Decisions
- Scaling Predictions and Extrapolation Limits
- Checkpoint Frequency and Recovery Cost
- Comparing Training Plans Under Fixed Budgets
70Instruction Tuning and Efficient Adaptation12 temas
- Instruction-Response Training Data
- Supervised Fine-Tuning Objectives
- Conversation Templates and Loss Masking
- Full-Parameter Fine-Tuning
- Adapters and Low-Rank Adaptation
- Quantized Base Models and Adapter Training
- Domain Adaptation and Continued Instruction Training
- Data Mixtures and Capability Retention
- Overfitting and Forgetting During Adaptation
- Adapter Composition and Deployment Constraints
- Evaluating Adaptation Against Prompting Baselines
- Versioning Adapted Models and Training Data
71Preference Learning and Post-Training12 temas
- Preference Data and Comparison Tasks
- Reward Model Training
- Human and Model-Generated Feedback
- Reinforcement Learning From Preference Signals
- Reference Policies and Divergence Constraints
- Direct Preference Optimization
- Verifiable Rewards and Outcome Feedback
- Process Feedback and Intermediate Evaluation
- Reward Overoptimization and Proxy Failures
- Preference Diversity and Annotation Bias
- Capability Retention During Post-Training
- Evaluating Helpfulness, Reliability, and Behavioral Boundaries
72Language Model Inference and Structured Generation12 temas
- Autoregressive Decoding and Token Probabilities
- Greedy, Beam, and Sampling-Based Decoding
- Temperature and Probability Truncation
- Length, Repetition, and Stop Conditions
- Prefill and Token-by-Token Generation
- Key-Value Caching
- Streaming and Partial Outputs
- Grammar-Constrained and Schema-Constrained Generation
- Structured Output Validation
- Tool Call Representations
- Decoding Reproducibility and Nondeterminism
- Latency, Cost, and Output Quality Tradeoffs
73Prompting and Context Engineering12 temas
- Task Instructions and Success Criteria
- Zero-Shot and Few-Shot Examples
- Demonstration Selection and Ordering
- Separating Instructions From Reference Material
- Context Budgets and Information Prioritization
- Document Selection and Context Assembly
- Conversation State and Summarized History
- Structured Inputs and Output Contracts
- Asking for Evidence and Verifiable Results
- Prompt Sensitivity and Controlled Experiments
- Context Failure Modes and Conflicting Instructions
- Versioning and Evaluating Prompt Templates
74Neural Retrieval and Semantic Search12 temas
- Query and Document Representation Learning
- Bi-Encoders and Cross-Encoders
- Contrastive Retrieval Training
- In-Batch and Hard Negative Sampling
- Dense, Sparse, and Hybrid Retrieval
- Approximate Nearest-Neighbor Index Interfaces
- Chunk and Document-Level Retrieval
- Candidate Generation and Reranking
- Query Reformulation and Expansion
- Retrieval Recall and Ranking Quality
- Domain Adaptation of Embedding Models
- Index Freshness and Representation Version Changes
75Retrieval-Augmented Generation12 temas
- Retrieval, Context Assembly, and Grounded Generation
- Document Parsing and Chunking Strategies
- Metadata Filters and Access-Aware Retrieval
- Multi-Stage Retrieval Pipelines
- Query Decomposition and Multi-Hop Retrieval
- Grounding Answers in Retrieved Evidence
- Citations and Source Attribution Quality
- Missing, Stale, and Contradictory Evidence
- Abstention When Retrieval Is Insufficient
- Evaluating Retrieval and Generation Separately
- Comparing RAG, Long Context, and Fine-Tuning
- End-to-End RAG Error Analysis
76Reasoning and Inference-Time Computation12 temas
- Task Decomposition and Intermediate Representations
- Search Over Candidate Solutions
- Multiple Samples and Self-Consistency
- Correlated Errors Across Generated Solutions
- Verifiers and Outcome-Based Selection
- External Calculators, Solvers, and Execution Feedback
- Reasoning With Structured Constraints
- Inference-Time Compute Allocation
- Learned Reasoning and Generalization Limits
- Reported Rationales and Faithful Explanations
- Evaluating Reasoning on Verifiable Tasks
- Benchmark Contamination and Apparent Reasoning Gains
77Tool-Using AI Agents12 temas
- Agent Loops and Environment Interfaces
- Tool Definitions and Argument Schemas
- Planning, Acting, Observing, and Revising
- Tool Selection and Action Validation
- Permissions and Scoped Capabilities
- Instruction Authority and Untrusted Tool Results
- Timeouts, Retries, and Idempotent Actions
- Sandboxed Code Execution Interfaces
- Human Approval for Consequential Actions
- Termination Conditions and Resource Budgets
- Recovery From Tool Errors
- Recording Agent Actions and Evidence
78Agent Memory and Multi-Agent Systems12 temas
- Working Context and Persistent Memory
- Memory Selection, Summarization, and Retrieval
- Memory Freshness and Contradiction Handling
- Separating User Facts From Model Inferences
- Workflow Graphs and Agent Coordination
- Specialized Roles and Task Delegation
- Shared State and Communication Protocols
- Multi-Agent Disagreement and Resolution
- Cascading Errors and Shared Blind Spots
- Centralized and Distributed Agent Supervision
- Comparing Multi-Agent Designs With Simpler Workflows
- Coordinated Resource and Permission Limits
79Agent Evaluation and Reliability12 temas
- Task Success and Environment-State Verification
- Tool Choice and Argument Accuracy
- Trajectory-Level Evaluation
- Simulated Environments and Replayable Tasks
- Partial Progress and Recovery Metrics
- Long-Horizon Error Accumulation
- User Intervention and Supervision Requirements
- Cost, Latency, and Reliability Budgets
- Adversarial and Unexpected Tool Responses
- Evaluating Memory and Delegation Failures
- Regression Suites for Agent Behavior
- Deployment Boundaries From Evaluation Evidence
Etapa 6
Production, Trust & Research
IA que aguanta en el mundo real
15 módulos · 178 temas
80Online and Continual Learning11 temas
- Streaming Data and Incremental Updates
- Stationary and Changing Learning Environments
- Catastrophic Forgetting
- Experience Replay and Memory Selection
- Regularization-Based Continual Learning
- Expanding and Modular Model Architectures
- Task, Domain, and Class Incremental Settings
- Measuring Forgetting and Forward Transfer
- Update Frequency and Stability Tradeoffs
- Evaluation Without Access to Future Data
- Controlled Model Updates and Rollback Criteria
81Federated and Privacy-Preserving Learning12 temas
- Centralized and Federated Training Assumptions
- Client Sampling and Local Optimization
- Federated Averaging and Aggregation Variants
- Nonidentical Client Data Distributions
- Communication and Computation Tradeoffs
- Personalization Across Clients
- Secure Aggregation Interfaces
- Differential Privacy and Privacy Budgets
- Clipping, Noise, and Utility Tradeoffs
- Privacy Accounting Across Training Steps
- Evaluating Leakage and Privacy Limitations
- Robustness to Unreliable Client Updates
82Causal and Invariant Learning11 temas
- Predictive Association and Causal Structure
- Structural Causal Models for Learning Problems
- Interventions and Counterfactual Queries
- Causal Discovery Assumptions
- Confounding and Selection in Training Data
- Invariant Prediction Across Environments
- Causal Representation Learning Concepts
- Counterfactual Data Augmentation
- Causal Models for Decision Policies
- Testing Transportability and Domain Assumptions
- Limits of Causal Claims From Observational Learning
83Neurosymbolic and Constraint-Guided Learning11 temas
- Combining Neural Representations With Symbolic Structure
- Learned Perception and Logical Reasoning Interfaces
- Rules as Features, Constraints, and Loss Terms
- Differentiable Reasoning Components
- Neural Program Synthesis Concepts
- Learning to Guide Symbolic Search
- Knowledge Graphs in Neural Predictions
- Constraint Satisfaction for Generated Outputs
- Verifiable Intermediate Representations
- Tradeoffs Between Flexibility and Formal Structure
- Evaluating Generalization Beyond Training Templates
84Scientific and Physics-Informed Machine Learning12 temas
- Scientific Data and Domain Constraints
- Surrogate Models for Expensive Simulations
- Physics-Informed Training Objectives
- Boundary and Initial Condition Constraints
- Neural Operators and Function-to-Function Learning
- Symmetry-Aware Scientific Representations
- Learning Dynamical Systems
- Inverse Problems and Parameter Identification
- Multi-Fidelity and Simulation-Based Training
- Physical Validity and Conservation Checks
- Uncertainty in Scientific Model Predictions
- Evaluating Extrapolation Beyond Observed Conditions
85Model Compression and Edge Intelligence12 temas
- Model Size, Compute, and Memory Footprint
- Weight and Activation Quantization
- Post-Training and Quantization-Aware Methods
- Structured and Unstructured Pruning
- Knowledge Distillation Objectives
- Low-Rank and Factorized Model Approximations
- Sparse Computation and Hardware Realities
- Compact Architectures for On-Device Inference
- Accuracy, Latency, Energy, and Privacy Tradeoffs
- Calibration Data for Compression
- Verifying Compressed Models Across Input Slices
- Compression Effects on Rare and Sensitive Behaviors
86Distributed and Efficient Model Training12 temas
- Training Memory for Parameters, Gradients, and Optimizers
- Data Parallelism and Gradient Synchronization
- Sharded Parameters and Optimizer States
- Tensor and Pipeline Parallelism
- Expert Parallelism in Sparse Models
- Communication Overhead and Scaling Efficiency
- Activation Checkpointing and Recomputation
- Mixed Precision and Numerical Consistency
- Efficient Attention and Memory Access
- Input Pipeline and Accelerator Utilization
- Distributed Checkpoints and Failure Recovery
- Correctness Checks for Parallel Training
87Model Inference and Serving12 temas
- Batch, Online, and Streaming Prediction
- Model Input and Output Contracts
- Export Formats and Runtime Compatibility
- Dynamic Batching and Request Scheduling
- Throughput and Tail Latency
- Model and Result Caching
- Language Model Cache Management
- Speculative Decoding and Verification
- Routing Between Models of Different Capabilities
- Resource Limits and Graceful Degradation
- Numerical Parity Across Inference Runtimes
- Quality-Aware Inference Optimization
88Machine Learning Lifecycle and Monitoring12 temas
- Data, Code, Model, and Configuration Lineage
- Experiment Tracking and Model Registries
- Reproducible Training and Evaluation Pipelines
- Training-Serving Consistency
- Model Release Criteria and Review Evidence
- Shadow and Limited-Rollout Evaluation
- Data Drift and Prediction Drift
- Delayed Labels and Performance Monitoring
- Model Degradation and Retraining Triggers
- Feedback Loops After Deployment
- Rollback and Model Retirement
- Model Cards and Operational Documentation
89Uncertainty, Robustness, and Distribution Shift12 temas
- Aleatoric and Epistemic Uncertainty
- Deep Ensembles and Approximate Bayesian Methods
- Calibration Under Distribution Changes
- Conformal Prediction and Exchangeability Assumptions
- Prediction Sets and Coverage Evaluation
- Out-of-Distribution Detection
- Corruptions, Perturbations, and Natural Variation
- Spurious Correlations and Shortcut Dependence
- Distributionally Robust Objectives
- Abstention and Escalation Strategies
- Stress Testing Across Plausible Deployment Conditions
- Distinguishing Confidence From Correctness
90Interpretability and Model Understanding12 temas
- Global and Local Explanations
- Intrinsically Interpretable Model Choices
- Feature Importance and Permutation Tests
- Partial Dependence and Correlated Inputs
- Local Surrogates and Attribution Methods
- Gradient-Based Explanations
- Counterfactual Explanations and Feasibility
- Concept-Based Interpretation
- Probing Learned Representations
- Mechanistic Interpretability and Activation Interventions
- Faithfulness, Stability, and Explanation Evaluation
- Limits of Interpreting Model-Generated Explanations
91Fairness and Human Impact12 temas
- Stakeholders and Affected Populations
- Sources of Bias Across the Model Lifecycle
- Group and Individual Fairness Concepts
- Fairness Metrics and Incompatible Objectives
- Subgroup Performance and Intersectional Analysis
- Bias Mitigation Through Data and Training Choices
- Accessibility and Unequal Error Burdens
- Human Review and Decision Authority
- Automation Bias and Overreliance
- Contestability and Correcting Model Errors
- Documenting Tradeoffs and Unresolved Limitations
- Evaluating Whether an AI System Should Be Used
92AI Security and Misuse Risk12 temas
- Threat Models for Learned Systems
- Adversarial Input and Evasion Risks
- Training Data Poisoning and Backdoors
- Model Extraction and Information Leakage
- Prompt Injection in Retrieval and Tool Workflows
- Untrusted Models, Datasets, and Dependencies
- Capability Boundaries and Least-Privilege Integration
- Misuse Evaluation and Defensive Testing
- Content Safeguards and Their Limitations
- Monitoring Abuse and Unexpected System Behavior
- Incident Response for Compromised AI Components
- Evidence-Based Release and Access Decisions
93Foundation Model Evaluation13 temas
- Capability-Specific and Application-Specific Test Sets
- Benchmark Validity and Data Contamination
- Factual Accuracy and Unsupported Claims
- Instruction Following and Constraint Satisfaction
- Multilingual and Cross-Domain Performance
- Long-Context Retrieval and Information Use
- Human Evaluation and Annotation Agreement
- Model-Based Judges and Judge Bias
- Pairwise Preferences and Ranking Uncertainty
- Robustness to Prompt and Context Variations
- Safety, Refusal, and Over-Refusal Evaluation
- Cost and Latency Alongside Quality
- Evaluation Changes After Adaptation and Compression
94AI Research and Reproducible Evidence12 temas
- Reading Research Claims and Experimental Designs
- Separating Contributions From Implementation Choices
- Reproducing Baselines Before Extensions
- Ablations and Controlled Comparisons
- Reporting Data, Compute, and Search Budgets
- Random Seeds and Result Variability
- Negative Results and Failed Hypotheses
- Benchmark Overfitting and Selective Reporting
- Dataset and Model Documentation
- Releasing Reproducible Artifacts
- Identifying Unsupported Generalization Claims
- Turning Research Results Into Engineering Decisions
Etapa 7
Practicum & Capstone
Tu propio sistema, de principio a fin
4 módulos · 43 temas
95Classical AI and Predictive Learning Practicum10 temas
- Formulating a Search or Planning Problem
- Comparing Heuristics Under a Fixed Budget
- Building a Constraint-Based Solution
- Preparing a Supervised Learning Dataset
- Establishing Linear and Tree-Based Baselines
- Comparing Kernel and Ensemble Models
- Designing Leakage-Resistant Evaluation
- Tuning Models Under Resource Constraints
- Analyzing Calibration and Failure Slices
- Presenting a Reproducible Model Selection Report
96Deep and Generative Learning Practicum10 temas
- Implementing and Verifying a Neural Training Loop
- Comparing Neural Architecture Choices
- Training a Self-Supervised Representation
- Adapting a Pretrained Model to a Defined Task
- Building a Small Generative Model
- Constructing a Minimal Transformer Language Model
- Evaluating Data Quality and Training Stability
- Testing Generation Quality and Diversity
- Measuring Compression and Inference Tradeoffs
- Producing a Documented Deep Learning Experiment
97Decision-Making and Agent Evaluation Practicum10 temas
- Defining a Sequential Decision Environment
- Comparing Bandit or Reinforcement Learning Baselines
- Evaluating Policies on Held-Out Scenarios
- Building a Retrieval-Grounded Task Workflow
- Integrating Tools With Explicit Action Boundaries
- Testing Memory and Multi-Step Task Execution
- Simulating Tool Failures and Conflicting Evidence
- Measuring Task Success, Cost, and Intervention Needs
- Building an Agent Regression Suite
- Reporting Reliability Limits and Deployment Conditions
98Integrated Artificial Intelligence Engineering Project13 temas
- Defining the User Need and AI System Boundary
- Establishing Data Access and Evaluation Requirements
- Selecting Symbolic, Statistical, Neural, or Hybrid Methods
- Building Baselines and a Reproducible Training Workflow
- Developing the Model and Task-Specific Adaptation
- Designing Retrieval, Tools, or Decision Logic Where Needed
- Evaluating Quality, Uncertainty, and Failure Modes
- Testing Fairness, Security, and Human Oversight Requirements
- Optimizing Resource Use and Inference Performance
- Preparing Model Interfaces and Release Evidence
- Defining Monitoring, Update, and Rollback Procedures
- Documenting Data, Models, Assumptions, and Limitations
- Presenting a Complete AI System and Evaluation Package
Quince minutos.Cada día.
- 1
Una lección cabe en la pausa de la comida
Una idea cada vez, en diapositivas breves. Una lección entera dura unos quince minutos.
- 2
Práctica con corrección al instante
Las preguntas van dentro de la lección. Responde y ve al instante si lo has entendido.
- 3
Una racha que te hace volver
Una lección al día mantiene viva la racha. Sesiones cortas y constantes te llevan hasta el final.
- 1
Una lección cabe en la pausa de la comida
Una idea cada vez, en diapositivas breves. Una lección entera dura unos quince minutos.
- 2
Práctica con corrección al instante
Las preguntas van dentro de la lección. Responde y ve al instante si lo has entendido.
- 3
Una racha que te hace volver
Una lección al día mantiene viva la racha. Sesiones cortas y constantes te llevan hasta el final.
Pensado para quien construye.
Ingenieros que se pasan al ML
Donde más vas a crecer
Construye y depura redes neuronales profundas
Etapa 4 · Deep & Reinforcement LearningAnalistas listos para construir modelos
Equipos de producto que lanzan funciones de IA
Donde más vas a crecer
Lanza pipelines RAG y agentes que usan herramientas
Etapa 5 · Generative AI & Agents
A dónde lleva este curso.
El trabajo en torno al que está construido este curso, y cómo se entra en él.
Machine Learning Engineer
Construye, entrena y lanza los modelos que hay detrás de un producto, y los mantiene funcionando después del lanzamiento.
Todas las carreras del futuroEn el trabajo
- Convertir una pregunta de producto en un conjunto de datos y un modelo
- Evaluar un modelo con honestidad antes de que llegue a los usuarios
- Poner un modelo en producción y vigilar su deriva
Cómo se entra
Normalmente, un título en informática, matemáticas o un campo afín, o una base sólida en software más un portafolio de modelos en producción.
Sé de los primeros.
Acceso anticipado para particulares, pilotos para equipos. Cuéntanos quién va a aprender.
enterprise@astratrainer.com