Machine Learning
Contents I. Introduction
Objectives
Related Fields 1. Statistics
Literature Machine Learning:
Literature Machine Learning:
Software Programming:
Software Machine Learning:
Software Statistics:
Software Lab Class Setup
Software Lab Class Setup
Chapter ML:I I. Introduction
Examples of Learning Tasks (1) Car Shopping Guide
Examples of Learning Tasks (2) Risk Analysis for Credit Approval
Examples of Learning Tasks (2) Risk Analysis for Credit Approval
Examples of Learning Tasks (3) Image Analysis
Examples of Learning Tasks (3) Image Analysis
Chapter ML:I I. Introduction
Specification of Learning Tasks Definition 1 (Machine Learning
Remarks:
Specification of Learning Tasks Learning Paradigms
Specification of Learning Tasks Learning Paradigms
Specification of Learning Tasks (4) Example Chess: Kinds of Experience
Specification of Learning Tasks (4) Example Chess: Kinds of Experience
Specification of Learning Tasks (4) Example Chess: Kinds of Experience
Specification of Learning Tasks (4) Example Chess: Ideal Target Function γ()
Specification of Learning Tasks (4) Example Chess: Ideal Target Function γ()
Specification of Learning Tasks (4) Example Chess: Real World γ() → Model World y()
Specification of Learning Tasks (4) Example Chess: Real World γ() → Model World y()
Specification of Learning Tasks (4) Example Chess: Real World γ() → Model World y()
Remarks:
Remarks: (continued)
Specification of Learning Tasks
Specification of Learning Tasks
Specification of Learning Tasks Real World → Model World
Specification of Learning Tasks Real World → Model World
Specification of Learning Tasks Real World → Model World
Specification of Learning Tasks Real World → Model World
Specification of Learning Tasks Real World → Model World
Remarks:
Remarks (continued) :
Specification of Learning Tasks The LMS Algorithm for Fitting y(x)
Specification of Learning Tasks The LMS Algorithm for Fitting y(x)
Remarks:
Remarks (continued):
Chapter ML:I I. Introduction
Elements of Machine Learning (1) Model Formation: Real World → Model World
Elements of Machine Learning (2) ML Stack: LMS
Elements of Machine Learning (2) ML Stack: LMS
Elements of Machine Learning (2) ML Stack: LMS
Elements of Machine Learning (2) ML Stack: LMS
Elements of Machine Learning (2) ML Stack: LMS
Related questions:
Elements of Machine Learning (3) Feature Space Structure
Elements of Machine Learning (3) Feature Space Structure
Elements of Machine Learning (3) Feature Space Structure
Remarks:
Elements of Machine Learning (4) Discriminative versus Generative Approach to Classification
Elements of Machine Learning (4) Discriminative versus Generative Approach to Classification
Elements of Machine Learning (4) Discriminative versus Generative Approach to Classification
Remarks:
Elements of Machine Learning (5) Frequentist versus Subjectivist Paradigm to Learning
Elements of Machine Learning (5) Frequentist versus Subjectivist Paradigm to Learning
Elements of Machine Learning (5) Frequentist versus Subjectivist Paradigm to Learning
Remarks:
Elements of Machine Learning (5) Frequentist versus Subjectivist Paradigm to Learning
Elements of Machine Learning (5) Frequentist versus Subjectivist Paradigm to Learning
Elements of Machine Learning (5) Frequentist versus Subjectivist Paradigm to Learning
Elements of Machine Learning (5) Frequentist versus Subjectivist Paradigm to Learning
Elements of Machine Learning (5) Frequentist versus Subjectivist Paradigm to Learning
Elements of Machine Learning (5) Frequentist versus Subjectivist Paradigm to Learning
Remarks:
Chapter ML:I I. Introduction
Notation Overview Data, Sets, and Distributions
Notation Overview Indexing
Notation Overview Functions
Notation Overview Algorithms
Classification Approaches Overview Search in hypothesis space
Chapter ML:II
Rule-Based Learning of Simple Concepts Classification Problem
Rule-Based Learning of Simple Concepts Example Learning Task
Rule-Based Learning of Simple Concepts Example Learning Task
Remarks:
Rule-Based Learning of Simple Concepts Concepts and Hypotheses
Rule-Based Learning of Simple Concepts Concepts and Hypotheses
Remarks (concept) :
Remarks (hypothesis) :
Rule-Based Learning of Simple Concepts Consistent Hypotheses
Remarks:
Rule-Based Learning of Simple Concepts Decision Rules as Model Functions
Rule-Based Learning of Simple Concepts Decision Rules as Model Functions
Remarks:
Rule-Based Learning of Simple Concepts Extremal Hypotheses
Rule-Based Learning of Simple Concepts Order of Hypotheses
Rule-Based Learning of Simple Concepts Order of Hypotheses
Remarks:
Remarks (entailment) :
Remarks (entailment) : (continued)
Rule-Based Learning of Simple Concepts Inductive Learning Hypothesis
Rule-Based Learning of Simple Concepts Inductive Learning Hypothesis
Rule-Based Learning of Simple Concepts Find-S Algorithm
Rule-Based Learning of Simple Concepts Find-S Algorithm
Remarks:
Rule-Based Learning of Simple Concepts Find-S Algorithm: Illustration
Rule-Based Learning of Simple Concepts Find-S Algorithm: Illustration
Rule-Based Learning of Simple Concepts Find-S Algorithm: Illustration
Rule-Based Learning of Simple Concepts Find-S Algorithm: Illustration
Rule-Based Learning of Simple Concepts Find-S Algorithm: Discussion
Rule-Based Learning of Simple Concepts The Set of Consistent Hypothesis
Rule-Based Learning of Simple Concepts The Set of Consistent Hypothesis
Remarks:
Rule-Based Learning of Simple Concepts The Set of Consistent Hypothesis
Rule-Based Learning of Simple Concepts The Set of Consistent Hypothesis
Remarks:
Rule-Based Learning of Simple Concepts Candidate Elimination Algorithm
Rule-Based Learning of Simple Concepts Candidate Elimination Algorithm
Remarks:
Rule-Based Learning of Simple Concepts Candidate Elimination Algorithm: Illustration
Rule-Based Learning of Simple Concepts Candidate Elimination Algorithm: Illustration
Rule-Based Learning of Simple Concepts Candidate Elimination Algorithm: Illustration
Rule-Based Learning of Simple Concepts Candidate Elimination Algorithm: Illustration
Rule-Based Learning of Simple Concepts Candidate Elimination Algorithm: Illustration
Rule-Based Learning of Simple Concepts Candidate Elimination Algorithm: Discussion
Rule-Based Learning of Simple Concepts (1) Active Learning: Selecting Examples
Rule-Based Learning of Simple Concepts (1) Active Learning: Selecting Examples
Rule-Based Learning of Simple Concepts (2) Ensembles: Exploiting Partially Learned Concepts
Rule-Based Learning of Simple Concepts (2) Ensembles: Exploiting Partially Learned Concepts
Rule-Based Learning of Simple Concepts (2) Ensembles: Exploiting Partially Learned Concepts
Rule-Based Learning of Simple Concepts (2) Ensembles: Exploiting Partially Learned Concepts
Rule-Based Learning of Simple Concepts (2) Ensembles: Exploiting Partially Learned Concepts
Rule-Based Learning of Simple Concepts (3) Inductive Bias
Rule-Based Learning of Simple Concepts (3) Inductive Bias
Rule-Based Learning of Simple Concepts (3) Inductive Bias
Rule-Based Learning of Simple Concepts Inductive Bias
Rule-Based Learning of Simple Concepts Inductive Bias
Rule-Based Learning of Simple Concepts Inductive Bias
Chapter ML:II II. Machine Learning Basics
From Regression to Classification Regression versus Classification
From Regression to Classification Regression versus Classification
From Regression to Classification Regression versus Classification
From Regression to Classification The Linear Regression Model
From Regression to Classification The Linear Regression Model
From Regression to Classification The Linear Regression Model
Remarks (residuals):
Remarks (randomness and distributions):
From Regression to Classification One-Dimensional Feature Space
From Regression to Classification One-Dimensional Feature Space
From Regression to Classification One-Dimensional Feature Space
From Regression to Classification One-Dimensional Feature Space
From Regression to Classification One-Dimensional Feature Space
From Regression to Classification One-Dimensional Feature Space
From Regression to Classification One-Dimensional Feature Space
From Regression to Classification One-Dimensional Feature Space
From Regression to Classification Higher-Dimensional Feature Space
From Regression to Classification Higher-Dimensional Feature Space
From Regression to Classification Higher-Dimensional Feature Space
From Regression to Classification Higher-Dimensional Feature Space
From Regression to Classification Higher-Dimensional Feature Space
Remarks:
From Regression to Classification Linear Regression for Classification (illustrated for p = 1)
From Regression to Classification Linear Regression for Classification (illustrated for p = 1)
From Regression to Classification Linear Regression for Classification (illustrated for p = 1)
From Regression to Classification Linear Regression for Classification (illustrated for p = 1)
From Regression to Classification Linear Regression for Classification (illustrated for p = 1)
From Regression to Classification Linear Regression for Classification (illustrated for p = 1)
From Regression to Classification Linear Regression for Classification (illustrated for p = 1)
Remarks: (?) We consider the feature vector x in its extended form when used as operand in a scalar
From Regression to Classification Linear Regression for Classification (illustrated for p = 2)
From Regression to Classification Linear Regression for Classification (illustrated for p = 2)
From Regression to Classification Linear Regression for Classification (illustrated for p = 2)
From Regression to Classification Linear Regression for Classification (illustrated for p = 2)
From Regression to Classification Linear Regression for Classification (illustrated for p = 2)
From Regression to Classification Linear Regression for Classification (illustrated for p = 2)
From Regression to Classification Linear Regression for Classification (illustrated for p = 2)
Remarks:
From Regression to Classification Linear Model Function Variants
From Regression to Classification Linear Model Function Variants
From Regression to Classification Linear Model Function Variants
From Regression to Classification Linear Model Function Variants
From Regression to Classification Non-Linear Decision Boundaries
From Regression to Classification Non-Linear Decision Boundaries
From Regression to Classification Non-Linear Decision Boundaries
From Regression to Classification Non-Linear Decision Boundaries
From Regression to Classification Non-Linear Decision Boundaries
From Regression to Classification Methods of Least Squares: Iterative versus Direct Methods
From Regression to Classification Methods of Least Squares: Iterative versus Direct Methods
From Regression to Classification Methods of Least Squares: Iterative versus Direct Methods
Remarks:
From Regression to Classification Properties of the Solution
From Regression to Classification Properties of the Solution
Remarks:
Chapter ML:II II. Machine Learning Basics
Evaluating Effectiveness Misclassification Rate
Evaluating Effectiveness Misclassification Rate
Evaluating Effectiveness Misclassification Rate
Evaluating Effectiveness Misclassification Rate
Evaluating Effectiveness Misclassification Rate
Evaluating Effectiveness Misclassification Rate
Evaluating Effectiveness Misclassification Rate
Evaluating Effectiveness Misclassification Rate
Evaluating Effectiveness Misclassification Rate
Evaluating Effectiveness Misclassification Rate
Remarks:
Remarks: (continued)
Evaluating Effectiveness Misclassification Rate
Evaluating Effectiveness Misclassification Rate
Evaluating Effectiveness Misclassification Rate
Evaluating Effectiveness Misclassification Rate
Evaluating Effectiveness Misclassification Rate
Evaluating Effectiveness Illustration 1: Label Noise
Evaluating Effectiveness Illustration 1: Label Noise
Evaluating Effectiveness Illustration 1: Label Noise
Evaluating Effectiveness Illustration 1: Label Noise
Remarks:
Remarks: (continued)
Remarks (probability basics) :
Evaluating Effectiveness Illustration 2: Bayes [Optimal] Classifier and Bayes Error
Evaluating Effectiveness Illustration 2: Bayes [Optimal] Classifier and Bayes Error
Evaluating Effectiveness Illustration 2: Bayes [Optimal] Classifier and Bayes Error
Evaluating Effectiveness Illustration 2: Bayes [Optimal] Classifier and Bayes Error
Evaluating Effectiveness Illustration 2: Bayes [Optimal] Classifier and Bayes Error
Remarks (Bayes classifier) :
Remarks (Bayes classifier) : (continued)
Evaluating Effectiveness Illustration 3: Marginal and Conditional Distributions
Evaluating Effectiveness Illustration 3: Marginal and Conditional Distributions
Evaluating Effectiveness Illustration 3: Marginal and Conditional Distributions
Evaluating Effectiveness Illustration 3: Marginal and Conditional Distributions
Evaluating Effectiveness Illustration 3: Marginal and Conditional Distributions
Evaluating Effectiveness Illustration 3: Marginal and Conditional Distributions
Remarks:
Evaluating Effectiveness Illustration 4: Probability Distribution in a Classifiction Setting
Evaluating Effectiveness Illustration 4: Probability Distribution in a Classifiction Setting
Evaluating Effectiveness Illustration 4: Probability Distribution in a Classifiction Setting
Evaluating Effectiveness Illustration 4: Probability Distribution in a Classifiction Setting
Evaluating Effectiveness Illustration 4: Probability Distribution in a Classifiction Setting
Evaluating Effectiveness Illustration 4: Probability Distribution in a Classifiction Setting
Remarks:
Evaluating Effectiveness Estimating Error Bounds
Evaluating Effectiveness Estimating Error Bounds
Evaluating Effectiveness Estimating Error Bounds
Evaluating Effectiveness Estimating Error Bounds
Remarks:
Evaluating Effectiveness Training Error
Remarks (training error) :
Evaluating Effectiveness Holdout Error
Evaluating Effectiveness Holdout Error
Evaluating Effectiveness Holdout Error
Evaluating Effectiveness Holdout Error
Remarks (holdout error) :
Remarks (holdout error) : (continued)
Evaluating Effectiveness k-Fold Cross-Validation
Evaluating Effectiveness k-Fold Cross-Validation
Remarks:
Evaluating Effectiveness Comparing Model Variants
Remarks:
Evaluating Effectiveness Model Selection: Single Validation Set
Evaluating Effectiveness Model Selection: Single Validation Set
Evaluating Effectiveness Model Selection: Single Validation Set
Evaluating Effectiveness Model Selection: k validation sets
Evaluating Effectiveness Model Selection: k validation sets
Evaluating Effectiveness Model Selection: k validation sets
Remarks:
Evaluating Effectiveness Misclassification Costs
Remarks:
Chapter ML:III III. Linear Models
Logistic Regression Binary Classification Problems
Logistic Regression Binary Classification Problems
Logistic Regression Linear Regression
Logistic Regression Linear Regression
Logistic Regression Linear Regression
Logistic Regression Linear Regression
Logistic Regression Linear Regression
Remarks: (?) Recap.
Logistic Regression Sigmoid (Logistic) Function
Logistic Regression Sigmoid (Logistic) Function
Logistic Regression Sigmoid (Logistic) Function
Logistic Regression Sigmoid (Logistic) Function
Logistic Regression Interpretation of the Logistic Model Function
Logistic Regression Interpretation of the Logistic Model Function
Logistic Regression Interpretation of the Logistic Model Function
Logistic Regression Interpretation of the Logistic Model Function
Logistic Regression Interpretation of the Logistic Model Function
Logistic Regression Interpretation of the Logistic Model Function
Logistic Regression Interpretation of the Logistic Model Function
Remarks (probabilistic view to classification) :
Remarks (probabilistic view to classification) : (continued)
Remarks (derivation of Lσ (w)) :
Remarks (derivation of Lσ (w)) : (continued) = argmax
Logistic Regression Recap. Linear Regression for Classification (illustrated for p = 2)
Logistic Regression Recap. Linear Regression for Classification (illustrated for p = 2)
Logistic Regression Logistic Regression for Classification (illustrated for p = 2)
Logistic Regression Logistic Regression for Classification (illustrated for p = 2)
Logistic Regression Logistic Regression for Classification (illustrated for p = 2)
Logistic Regression Logistic Regression for Classification (illustrated for p = 2)
Logistic Regression Logistic Regression for Classification (illustrated for p = 2)
Logistic Regression Logistic Regression for Classification (illustrated for p = 2)
Logistic Regression The BGDσ Algorithm
Logistic Regression The BGDσ Algorithm
Remarks (BGDσ Algorithm) :
Remarks: (continued)
Logistic Regression ML Stack: Logistic Regression
Logistic Regression ML Stack: Logistic Regression
Logistic Regression ML Stack: Logistic Regression
Logistic Regression ML Stack: Logistic Regression
Logistic Regression ML Stack: Logistic Regression
Logistic Regression Non-Linear Decision Boundaries
Logistic Regression Non-Linear Decision Boundaries
Logistic Regression Non-Linear Decision Boundaries
Logistic Regression Non-Linear Decision Boundaries
Logistic Regression Non-Linear Decision Boundaries
Remarks:
Chapter ML:III III. Linear Models
Loss Computation in Detail ML Stack: Loss Computation
Remarks (loss function) :
Remarks (different roles of loss functions) :
Loss Computation in Detail (1) Linear Regression
Loss Computation in Detail (1) Linear Regression
Loss Computation in Detail (1) Linear Regression
Loss Computation in Detail (1) Linear Regression
Loss Computation in Detail (1) Linear Regression
Loss Computation in Detail (1) Linear Regression
Loss Computation in Detail (1) Linear Regression
Remarks:
Loss Computation in Detail (2) Logistic Regression
Loss Computation in Detail (2) Logistic Regression
Loss Computation in Detail (2) Logistic Regression
Loss Computation in Detail (2) Logistic Regression
Remarks:
Chapter ML:III III. Linear Models
Overfitting Definition 9 (Overfitting)
Overfitting Definition 9 (Overfitting)
Overfitting Definition 9 (Overfitting)
Overfitting Definition 9 (Overfitting)
Overfitting Definition 9 (Overfitting)
Remarks:
Overfitting Example: Linear Regression
Overfitting Example: Linear Regression
Overfitting Example: Linear Regression
Overfitting Example: Linear Regression
Overfitting Example: Linear Regression
Overfitting Example: Linear Regression
Overfitting Example: Linear Regression
Overfitting Example: Linear Regression
Overfitting Definition 9 (Overfitting
Overfitting Definition 9 (Overfitting
Overfitting Definition 9 (Overfitting
Overfitting Definition 9 (Overfitting
Remarks:
Overfitting Mitigation Strategies
Overfitting Mitigation Strategies
Overfitting Mitigation Strategies
Chapter ML:III III. Linear Models
Regularization ML Stack: Regularization
Regularization Bound the Absolute Values of the Weights w
Regularization Bound the Absolute Values of the Weights w
Regularization Bound the Absolute Values of the Weights w
Regularization Bound the Absolute Values of the Weights w
Regularization Bound the Absolute Values of the Weights w
Remarks:
Remarks: (continued)
Regularization The Vector Norm as Regularization Function
Remarks:
Regularization The Vector Norm as Regularization Function
Regularization The Vector Norm as Regularization Function
Regularization The Vector Norm as Regularization Function
Remarks:
Regularization The Vector Norm as Regularization Function
Regularization The Vector Norm as Regularization Function
Regularization The Vector Norm as Regularization Function
Regularization The Vector Norm as Regularization Function
Regularization The Vector Norm as Regularization Function
Regularization The Vector Norm as Regularization Function
Regularization The Vector Norm as Regularization Function
Regularization The Vector Norm as Regularization Function
Regularization The Vector Norm as Regularization Function
Regularization The Vector Norm as Regularization Function
Remarks:
Regularization Regularized Linear Regression
Regularization Regularized Linear Regression
Regularization Regularized Linear Regression
Regularization Regularized Linear Regression
Regularization Regularized Linear Regression
Regularization Regularized Linear Regression
Regularization Regularized Linear Regression
Chapter ML:III III. Linear Models
Gradient Descent in Detail ML Stack: Gradient Descend
Gradient Descent in Detail Principle
Gradient Descent in Detail Principle
Gradient Descent in Detail Principle
Gradient Descent in Detail Principle
Remarks:
Gradient Descent in Detail
Gradient Descent in Detail
Gradient Descent in Detail
Gradient Descent in Detail
Gradient Descent in Detail
Gradient Descent in Detail (1) Linear Regression + Squared Loss
Gradient Descent in Detail (1) Linear Regression + Squared Loss
Remarks (derivation of ∇L2 (w)) :
Gradient Descent in Detail The BGD Algorithm
Gradient Descent in Detail The BGD Algorithm
Remarks: (?) Recap.
Gradient Descent in Detail Global Loss versus Pointwise Loss
Remarks:
Gradient Descent in Detail The IGD Algorithm
Gradient Descent in Detail The IGD Algorithm
Remarks (IGD) :
Remarks (:::::::: recap. different roles of loss functions) :
Gradient Descent in Detail (2) Linear Regression + 0/1 Loss
Gradient Descent in Detail (2) Linear Regression + 0/1 Loss
Gradient Descent in Detail [squared loss]
Gradient Descent in Detail [squared loss]
Remarks:
Gradient Descent in Detail [squared loss]
Gradient Descent in Detail [squared loss]
Gradient Descent in Detail [squared loss]
Gradient Descent in Detail [squared loss]
Gradient Descent in Detail [squared loss]
Gradient Descent in Detail (3) Logistic Regression + Logistic Loss + Regularization
Gradient Descent in Detail (3) Logistic Regression + Logistic Loss + Regularization
Remarks:
Remarks (derivation of ∇Lσ (w)) :
Remarks (derivation of ∇Lσ (w)) : (continued) X 
Chapter ML:IV IV. Neural Networks
Perceptron Learning Biological Inspiration
Perceptron Learning Biological Inspiration
Remarks (facts about the human brain):
Perceptron Learning History
Perceptron Learning The Perceptron of Rosenblatt (1958)
Perceptron Learning The Perceptron of Rosenblatt (1958)
Perceptron Learning The Perceptron of Rosenblatt (1958)
Perceptron Learning The Perceptron of Rosenblatt (1958)
Remarks:
Perceptron Learning Binary Classification Problems
Perceptron Learning The PT Algorithm
Perceptron Learning The PT Algorithm
Remarks: (?) Recap.
Perceptron Learning Weight Adaptation: Illustration in Input Space
Perceptron Learning Weight Adaptation: Illustration in Input Space
Remarks:
Perceptron Learning Example
Perceptron Learning Example
Perceptron Learning Example: Illustration in Input Space
Perceptron Learning Example: Illustration in Input Space
Perceptron Learning Example: Illustration in Input Space
Perceptron Learning Example: Illustration in Input Space
Perceptron Learning Example: Illustration in Input Space
Perceptron Learning Example: Illustration in Input Space
Perceptron Learning Example: Illustration in Input Space
Perceptron Learning Example: Illustration in Input Space
Perceptron Learning Example: Illustration in Input Space
Perceptron Learning Example: Illustration in Input Space
Perceptron Learning Perceptron Convergence Theorem
Perceptron Learning Perceptron Convergence Theorem
Perceptron Learning Perceptron Convergence Theorem: Proof
Perceptron Learning Perceptron Convergence Theorem: Proof
Perceptron Learning Perceptron Convergence Theorem: Proof
Perceptron Learning Perceptron Convergence Theorem: Proof
Perceptron Learning Perceptron Convergence Theorem: Proof
Perceptron Learning Perceptron Convergence Theorem: Proof
Perceptron Learning Perceptron Convergence Theorem: Proof
Perceptron Learning Perceptron Convergence Theorem: Proof
Perceptron Learning Perceptron Convergence Theorem: Proof
Perceptron Learning Perceptron Convergence Theorem: Proof
Perceptron Learning Perceptron Convergence Theorem: Proof
Perceptron Learning Perceptron Convergence Theorem: Proof
Perceptron Learning Perceptron Convergence Theorem: Proof
Perceptron Learning Perceptron Convergence Theorem: Proof
Perceptron Learning Perceptron Convergence Theorem: Discussion
Remarks:
Perceptron Learning PT Algorithm versus Regression
Perceptron Learning PT Algorithm versus Regression
Perceptron Learning PT Algorithm versus Regression
Remarks:
Remarks: (continued)
Chapter ML:IV IV. Neural Networks
Multilayer Perceptron Basics Definition 1 (Linear Separability)
Multilayer Perceptron Basics Definition 1 (Linear Separability)
Multilayer Perceptron Basics Linear Separability
Multilayer Perceptron Basics Linear Separability
Multilayer Perceptron Basics (1) Overcoming the Linear Separability Restriction
Multilayer Perceptron Basics (1) Overcoming the Linear Separability Restriction
Multilayer Perceptron Basics (1) Overcoming the Linear Separability Restriction
Multilayer Perceptron Basics (1) Overcoming the Linear Separability Restriction
Multilayer Perceptron Basics (1) Overcoming the Linear Separability Restriction
Multilayer Perceptron Basics (1) Overcoming the Linear Separability Restriction
Remarks:
Remarks (history) :
Multilayer Perceptron Basics (2) Overcoming the Non-Differentiability Restriction
Multilayer Perceptron Basics (2) Overcoming the Non-Differentiability Restriction
Remarks:
Multilayer Perceptron Basics (2) Overcoming the Non-Differentiability Restriction
Multilayer Perceptron Basics (2) Overcoming the Non-Differentiability Restriction
Multilayer Perceptron Basics (2) Overcoming the Non-Differentiability Restriction
Multilayer Perceptron Basics (2) Overcoming the Non-Differentiability Restriction
Remarks (limitation of linear thresholds) :
Multilayer Perceptron Basics Unrestricted Classification Problems
Multilayer Perceptron Basics Unrestricted Classification Problems: Illustration
Multilayer Perceptron Basics Unrestricted Classification Problems: Illustration
Chapter ML:IV IV. Neural Networks
Multilayer Perceptron with Two Layers Network Architecture
Multilayer Perceptron with Two Layers Network Architecture
Multilayer Perceptron with Two Layers Network Architecture
Multilayer Perceptron with Two Layers Network Architecture
Multilayer Perceptron with Two Layers (1) Forward Propagation
Remarks:
Multilayer Perceptron with Two Layers (1) Forward Propagation
Multilayer Perceptron with Two Layers (1) Forward Propagation
Multilayer Perceptron with Two Layers (1) Forward Propagation: Batch Mode
Multilayer Perceptron with Two Layers (2) Backpropagation
Multilayer Perceptron with Two Layers (2) Backpropagation
Multilayer Perceptron with Two Layers (continued)
Multilayer Perceptron with Two Layers (continued)
Multilayer Perceptron with Two Layers (continued)
Remarks:
Multilayer Perceptron with Two Layers (2) Backpropagation
Multilayer Perceptron with Two Layers (2) Backpropagation
Multilayer Perceptron with Two Layers (2) Backpropagation
Multilayer Perceptron with Two Layers The IGD Algorithm
Multilayer Perceptron with Two Layers The IGD Algorithm
Multilayer Perceptron with Two Layers The IGD Algorithm
Multilayer Perceptron with Two Layers The IGD Algorithm
Multilayer Perceptron with Two Layers The IGD Algorithm
Multilayer Perceptron with Two Layers The IGD Algorithm
Remarks:
Chapter ML:IV IV. Neural Networks
Multilayer Perceptron at Arbitrary Depth Network Architecture
Multilayer Perceptron at Arbitrary Depth (1) Forward Propagation
Multilayer Perceptron at Arbitrary Depth (2) Backpropagation
Multilayer Perceptron at Arbitrary Depth (2) Backpropagation
Multilayer Perceptron at Arbitrary Depth (2) Backpropagation
Multilayer Perceptron at Arbitrary Depth (2) Backpropagation
Multilayer Perceptron at Arbitrary Depth (2) Backpropagation
Multilayer Perceptron at Arbitrary Depth The IGD Algorithm
Multilayer Perceptron at Arbitrary Depth The IGD Algorithm
Multilayer Perceptron at Arbitrary Depth The IGD Algorithm
Multilayer Perceptron at Arbitrary Depth The IGD Algorithm
Multilayer Perceptron at Arbitrary Depth The IGD Algorithm
Multilayer Perceptron at Arbitrary Depth The IGD Algorithm
Remarks (derivation of ∇hs L2 (w)) :
Remarks (derivation of ∇hs L2 (w)) : (continued)
Remarks (derivation of ∇hs L2 (w)) : (continued)
Remarks (derivation of ∇hs L2 (w)) : (continued)
Remarks (derivation of ∇hs L2 (w)) : (continued)
Remarks (derivation of ∇hs L2 (w)) : (continued)
Remarks (derivation of ∇o L2 (w) and ∇h L2 (w) for MLP at depth one) :
Chapter ML:IV IV. Neural Networks
Advanced MLPs Loss Function: Cross-Entropy
Advanced MLPs Loss Function: Cross-Entropy
Advanced MLPs Loss Function: Cross-Entropy
Advanced MLPs Loss Function: Cross-Entropy
Advanced MLPs Loss Function: Cross-Entropy
Advanced MLPs Loss Function: Cross-Entropy
Remarks (cross-entropy, Kullback-Leibler divergence) :
Advanced MLPs Output Normalization: Softmax
Advanced MLPs Output Normalization: Softmax
Advanced MLPs Output Normalization: Softmax
Advanced MLPs Output Normalization: Softmax
Advanced MLPs Output Normalization: Softmax
Remarks (softmax) :
Advanced MLPs Relation to Logistic Regression
Advanced MLPs Relation to Logistic Regression
Advanced MLPs Relation to Logistic Regression
Remarks (softmax and logistic regression) :
Advanced MLPs Cross-Entropy in Classification Settings
Advanced MLPs Cross-Entropy in Classification Settings
Advanced MLPs Cross-Entropy in Classification Settings
Advanced MLPs Cross-Entropy in Classification Settings
Advanced MLPs Cross-Entropy in Classification Settings
Remarks (cross-entropy for classification) :
Advanced MLPs Activation Function: Rectified Linear Unit (ReLU)
Advanced MLPs Regularization: Dropout
Advanced MLPs Learning Rate Adaptation: Momentum
Advanced MLPs Learning Rate Adaptation: Momentum
Remarks:
Chapter ML:IV IV. Neural Networks
Automatic Gradient Computation The IGD Algorithm
Automatic Gradient Computation The IGD Algorithm
Automatic Gradient Computation The IGD Algorithm
Automatic Gradient Computation Reverse-Mode Automatic Differentiation in Computational Graphs
Automatic Gradient Computation Reverse-Mode Automatic Differentiation in Computational Graphs
Remarks:
Automatic Gradient Computation Autodiff Example: Setting
Automatic Gradient Computation Autodiff Example: Computational Graph
Automatic Gradient Computation Autodiff Example: Forward and Reverse Trace
Automatic Gradient Computation Autodiff Example: Forward and Reverse Trace
Automatic Gradient Computation Autodiff Example: Forward and Reverse Trace
Automatic Gradient Computation Autodiff Example: Forward and Reverse Trace
Automatic Gradient Computation Autodiff Example: Forward and Reverse Trace
Automatic Gradient Computation Autodiff Example: Forward and Reverse Trace
Automatic Gradient Computation Autodiff Example: Forward and Reverse Trace
Remarks:
Automatic Gradient Computation Reverse-mode Autodiff Algorithm for Scalar-valued Functions
Remarks:
Chapter ML:VI VI. Decision Trees
Decision Trees Basics Classification Problems with Nominal Features
Decision Trees Basics Decision Tree for the Concept “EnjoySurfing”
Decision Trees Basics Decision Tree for the Concept “EnjoySurfing”
Decision Trees Basics Definition 1 (Splitting, Induced Splitting)
Decision Trees Basics Definition 1 (Splitting, Induced Splitting)
Decision Trees Basics Definition 1 (Splitting, Induced Splitting)
Decision Trees Basics Definition 1 (Splitting, Induced Splitting)
Decision Trees Basics Definition 1 (Splitting, Induced Splitting)
Decision Trees Basics Definition 1 (Splitting, Induced Splitting)
Decision Trees Basics Definition 1 (Splitting, Induced Splitting)
Decision Trees Basics Definition 1 (Splitting, Induced Splitting)
Remarks:
Decision Trees Basics Definition 2 (Decision Tree)
Decision Trees Basics Definition 2 (Decision Tree)
Decision Trees Basics Definition 2 (Decision Tree)
Remarks:
Decision Trees Basics Notation
Remarks:
Decision Trees Basics Algorithm Template: Construction
Decision Trees Basics Algorithm Template: Construction
Decision Trees Basics Algorithm Template: Construction
Remarks:
Decision Trees Basics Algorithm Template: Classification
Remarks:
Decision Trees Basics When to Use Decision Trees
Decision Trees Basics When to Use Decision Trees
Decision Trees Basics On the Construction of Decision Trees
Decision Trees Basics Assessment of Decision Trees
Decision Trees Basics Assessment of Decision Trees
Decision Trees Basics Assessment of Decision Trees: Size
Decision Trees Basics Assessment of Decision Trees: Size
Decision Trees Basics Assessment of Decision Trees: Size
Decision Trees Basics Assessment of Decision Trees: Size
Decision Trees Basics Assessment of Decision Trees: Size
Decision Trees Basics Assessment of Decision Trees: Size
Decision Trees Basics Assessment of Decision Trees: Classification Error
Decision Trees Basics Assessment of Decision Trees: Classification Error
Decision Trees Basics Assessment of Decision Trees: Classification Error
Decision Trees Basics Assessment of Decision Trees: Classification Error
Remarks:
Remarks (misclassification costs) :
Chapter ML:VI VI. Decision Trees
Impurity Functions Splitting
Impurity Functions Splitting
Impurity Functions Splitting
Impurity Functions Splitting
Impurity Functions Definition 4 (Impurity Function ι)
Impurity Functions Definition 5 (Impurity of an Example Set ι(D))
Impurity Functions Definition 5 (Impurity of an Example Set ι(D))
Remarks:
Impurity Functions (1) Impurity Functions Based on the Misclassification Rate
Impurity Functions (1) Impurity Functions Based on the Misclassification Rate
Impurity Functions (1) Impurity Functions Based on the Misclassification Rate
Impurity Functions (1) Impurity Functions Based on the Misclassification Rate
Impurity Functions (1) Impurity Functions Based on the Misclassification Rate
Impurity Functions (1) Impurity Functions Based on the Misclassification Rate
Impurity Functions (1) Impurity Functions Based on the Misclassification Rate
Impurity Functions Definition 7 (Strict Impurity Function)
Impurity Functions Definition 7 (Strict Impurity Function)
Remarks:
Remarks: (continued)
Impurity Functions (2) Impurity Functions Based on Entropy
Remarks:
Impurity Functions (2) Impurity Functions Based on Entropy
Impurity Functions (2) Impurity Functions Based on Entropy
Impurity Functions (2) Impurity Functions Based on Entropy
Remarks:
Remarks: (continued)
Impurity Functions (2) Impurity Functions Based on Entropy
Impurity Functions (2) Impurity Functions Based on Entropy
Impurity Functions (2) Impurity Functions Based on Entropy
Impurity Functions (2) Impurity Functions Based on Entropy
Impurity Functions (2) Impurity Functions Based on Entropy
Impurity Functions (2) Impurity Functions Based on Entropy
Impurity Functions (2) Impurity Functions Based on Entropy
Impurity Functions (3) Impurity Functions Based on the Gini Index
Impurity Functions (3) Impurity Functions Based on the Gini Index
Impurity Functions (3) Impurity Functions Based on the Gini Index
Impurity Functions (3) Impurity Functions Based on the Gini Index
Chapter ML:VI VI. Decision Trees
Decision Tree Algorithms ID3 Algorithm
Decision Tree Algorithms ID3 Algorithm
Decision Tree Algorithms ID3 Algorithm
Decision Tree Algorithms ID3 Algorithm
Decision Tree Algorithms ID3 Algorithm
Decision Tree Algorithms ID3 Algorithm
Decision Tree Algorithms ID3 Algorithm
Decision Tree Algorithms ID3 Algorithm
Remarks:
Decision Tree Algorithms ID3 Algorithm: Example
Decision Tree Algorithms ID3 Algorithm: Example
Decision Tree Algorithms ID3 Algorithm: Example
Decision Tree Algorithms ID3 Algorithm: Example
Decision Tree Algorithms ID3 Algorithm: Example
Remarks:
Decision Tree Algorithms ID3 Algorithm: Example
Decision Tree Algorithms ID3 Algorithm: Example
Decision Tree Algorithms ID3 Algorithm: Example
Decision Tree Algorithms ID3 Algorithm: Search Space
Remarks (search space versus hypothesis space) :
Decision Tree Algorithms ID3 Algorithm: Inductive Bias
Decision Tree Algorithms ID3 Algorithm: Inductive Bias
Decision Tree Algorithms ID3 Algorithm: Inductive Bias
Remarks (inductive bias) :
Decision Tree Algorithms CART Algorithm
Decision Tree Algorithms CART Algorithm
Decision Tree Algorithms CART Algorithm
Decision Tree Algorithms CART Algorithm
Remarks:
Decision Tree Algorithms CART Algorithm
Decision Tree Algorithms CART Algorithm
Decision Tree Algorithms CART Algorithm
Decision Tree Algorithms CART Algorithm
Decision Tree Algorithms CART Algorithm
Decision Tree Algorithms CART Algorithm
Decision Tree Algorithms CART Algorithm
Decision Tree Algorithms CART Algorithm
Chapter ML:VI VI. Decision Trees
Decision Tree Pruning Overfitting
Decision Tree Pruning Overfitting
Decision Tree Pruning Overfitting
Remarks:
Decision Tree Pruning Overfitting
Decision Tree Pruning Overfitting
Remarks:
Decision Tree Pruning Overfitting
Decision Tree Pruning (a) Stopping
Decision Tree Pruning (a) Stopping
Decision Tree Pruning (b) Pruning
Decision Tree Pruning (b) Pruning
Decision Tree Pruning (b) Pruning
Decision Tree Pruning (b) Pruning
Decision Tree Pruning (b) Pruning
Decision Tree Pruning (b) Pruning: Reduced Error Pruning
Decision Tree Pruning (b) Pruning: Reduced Error Pruning
Decision Tree Pruning (b) Pruning: Reduced Error Pruning
Remarks (pruning extensions) :
Chapter ML:VII VII. Bayesian Learning
Approaches to Probability Area Overview
Approaches to Probability Area Overview
Approaches to Probability Definition 1 (Random Experiment, Random Observation)
Approaches to Probability Definition 1 (Random Experiment, Random Observation)
Remarks:
Approaches to Probability Definition 2 (Sample Space, Event Space)
Approaches to Probability Definition 2 (Sample Space, Event Space)
Approaches to Probability Definition 2 (Sample Space, Event Space)
Approaches to Probability Definition 2 (Sample Space, Event Space)
Approaches to Probability Definition 3 (Important Event Types)
Approaches to Probability Definition 3 (Important Event Types)
Remarks:
Approaches to Probability How to Capture the Nature of Probability
Approaches to Probability How to Capture the Nature of Probability
Remarks:
Approaches to Probability How to Capture the Nature of Probability
Remarks:
Approaches to Probability How to Capture the Nature of Probability
Approaches to Probability How to Capture the Nature of Probability
Approaches to Probability How to Capture the Nature of Probability
Approaches to Probability How to Capture the Nature of Probability
Approaches to Probability How to Capture the Nature of Probability
Approaches to Probability How to Capture the Nature of Probability
Approaches to Probability How to Capture the Nature of Probability
Remarks:
Remarks (frequentist versus subjectivist) :
Approaches to Probability Axiomatic Approach to Probability
Approaches to Probability Axiomatic Approach to Probability
Approaches to Probability Axiomatic Approach to Probability
Approaches to Probability Axiomatic Approach to Probability
Approaches to Probability Axiomatic Approach to Probability
Remarks:
Chapter ML:VII VII. Bayesian Learning
Conditional Probability Basic Definition
Conditional Probability Basic Definition
Conditional Probability Basic Definition
Conditional Probability Basic Definition
Conditional Probability Basic Definition
Conditional Probability Basic Definition
Remarks (conditional probability) :
Remarks (conditional probability) : (continued)
Remarks (conditional event algebra) :
Conditional Probability Total Probability
Conditional Probability Total Probability
Conditional Probability Total Probability
Conditional Probability Total Probability
Conditional Probability Total Probability
Conditional Probability Total Probability
Conditional Probability Total Probability
Remarks:
Conditional Probability Independence of Events
Conditional Probability Independence of Events
Conditional Probability Independence of Events
Conditional Probability Independence of Events
Conditional Probability Independence of Events
Conditional Probability Independence of Events
Conditional Probability Independence of Events
Conditional Probability Independence of Events
Conditional Probability Independence of Events
Conditional Probability Independence of Events
Chapter ML:VII VII. Bayesian Learning
Bayes Classifier Generative Approach to Classification Problems
Bayes Classifier Bayes Theorem
Bayes Classifier Bayes Theorem
Bayes Classifier Bayes Theorem
Bayes Classifier Example: Reasoning About a Disease
Bayes Classifier Example: Reasoning About a Disease
Bayes Classifier Example: Reasoning About a Disease
Bayes Classifier Example: Reasoning About a Disease
Bayes Classifier Example: Reasoning About a Disease
Bayes Classifier Example: Reasoning About a Disease
Bayes Classifier Example: Reasoning About a Disease
Bayes Classifier Example: Reasoning About a Disease
Remarks (prior probability model) :
Bayes Classifier Combined Conditional Events: P (Ai | B1, . . . , Bp)
Bayes Classifier Combined Conditional Events: P (Ai | B1, . . . , Bp)
Bayes Classifier Combined Conditional Events: P (Ai | B1, . . . , Bp)
Remarks [information gain for classification] :
Remarks: (continued)
Bayes Classifier Naive Bayes
Bayes Classifier Naive Bayes
Bayes Classifier Naive Bayes
Remarks:
Remarks: (continued)
Bayes Classifier Naive Bayes
Bayes Classifier Naive Bayes
Bayes Classifier Naive Bayes
Remarks:
Bayes Classifier Naive Bayes: Classifier Construction Summary
Bayes Classifier Naive Bayes: Classifier Construction Summary
Remarks:
Bayes Classifier Naive Bayes: Example
Bayes Classifier Naive Bayes: Example
Bayes Classifier Naive Bayes: Example
Bayes Classifier Naive Bayes: Example
Bayes Classifier Naive Bayes: Example
Bayes Classifier Naive Bayes: Example
Remarks:
Chapter ML:VII VII. Bayesian Learning
Exploitation of Data Data Events
Exploitation of Data Data Events
Exploitation of Data Data Events
Exploitation of Data Data Events
Remarks (predictor-response setting) :
Remarks: (continued)
Exploitation of Data Data Set Illustration
Exploitation of Data Data Set Illustration
Exploitation of Data Data Set Illustration
Remarks:
Exploitation of Data Typical Learning Settings
Exploitation of Data Typical Learning Settings
Exploitation of Data Typical Learning Settings
Exploitation of Data Typical Learning Settings
Exploitation of Data Typical Learning Settings
Exploitation of Data Typical Learning Settings
Exploitation of Data Typical Learning Settings
Remarks (predictor-response vs. outcome-only setting) : (1),. . . , (4)
Remarks (discriminative vs. generative approach) : (1), (2), (3)
Remarks (discriminative vs. generative approach) : (continued) (4)
Remarks (MLE principle vs. Bayesian framework) : (1), (2), (3)
Exploitation of Data Learning Approaches Overview
Exploitation of Data Learning Approaches Overview
Exploitation of Data Learning Approaches Overview
Exploitation of Data Learning Approaches Overview
Remarks:
Chapter ML:VII VII. Bayesian Learning
Frequentist versus Subjectivist Logistic Regression versus Naive Bayes
Frequentist versus Subjectivist Logistic Regression versus Naive Bayes
Frequentist versus Subjectivist Logistic Regression versus Naive Bayes
Frequentist versus Subjectivist Logistic Regression versus Naive Bayes
Frequentist versus Subjectivist Logistic Regression versus Naive Bayes
Frequentist versus Subjectivist Logistic Regression versus Naive Bayes
Frequentist versus Subjectivist Logistic Regression versus Naive Bayes
Remarks:
Remarks: (continued)
Frequentist versus Subjectivist Logistic Regression versus Naive Bayes: Example
Frequentist versus Subjectivist Logistic Regression versus Naive Bayes: Example
Frequentist versus Subjectivist Logistic Regression versus Naive Bayes: Example
Frequentist versus Subjectivist Logistic Regression versus Naive Bayes: Conditional Class Probabilities
Frequentist versus Subjectivist Logistic Regression versus Naive Bayes: Conditional Class Probabilities
Frequentist versus Subjectivist Logistic Regression versus Naive Bayes: Conditional Class Probabilities
Frequentist versus Subjectivist Logistic Regression versus Naive Bayes: Conditional Class Probabilities
Frequentist versus Subjectivist Logistic Regression versus Naive Bayes: Conditional Class Probabilities
Frequentist versus Subjectivist Logistic Regression versus Naive Bayes: Conditional Class Probabilities
Frequentist versus Subjectivist Logistic Regression versus Naive Bayes: Conditional Class Probabilities
Frequentist versus Subjectivist Logistic Regression versus Naive Bayes: Conditional Class Probabilities
Remarks:
Frequentist versus Subjectivist Naive Bayes: Smoothing and Continuous Likelihoods
Frequentist versus Subjectivist Naive Bayes: Prior Probability Models
Frequentist versus Subjectivist Classification: Bayes Optimum versus MAP versus Ensemble
Frequentist versus Subjectivist Advanced Bayesian Decision Making
Frequentist versus Subjectivist Advanced Bayesian Decision Making
Frequentist versus Subjectivist Advanced Bayesian Decision Making
Frequentist versus Subjectivist Advanced Bayesian Decision Making
Frequentist versus Subjectivist Advanced Bayesian Decision Making
Remarks:
Chapter ML:IX IX. Deep Learning
Introduction to Deep Learning History
Introduction to Deep Learning History
Introduction to Deep Learning History
Introduction to Deep Learning History
Introduction to Deep Learning History
Introduction to Deep Learning History
Introduction to Deep Learning History
Introduction to Deep Learning Types of Learning Tasks
Introduction to Deep Learning Types of Learning Tasks
Remarks:
Chapter ML:IX IX. Deep Learning
Autoencoder Networks Representation Learning Task: Word Embedding
Autoencoder Networks Word Embedding with Autoencoders
Autoencoder Networks Representation Learning Task: Co-Occurrence Embedding (Word2Vec)
Chapter ML:IX IX. Deep Learning
Convolutional Neural Networks Image Classification Task
Convolutional Neural Networks Image Classification Task
Convolutional Neural Networks Image Classification Task
Convolutional Neural Networks Image Classification with CNNs
Convolutional Neural Networks Image Classification with CNNs
Convolutional Neural Networks Image Classification with CNNs
Convolutional Neural Networks Image Classification with CNNs
Convolutional Neural Networks Image Classification with CNNs
Convolutional Neural Networks Image Classification with CNNs
Remarks (computation of yc ) :
Remarks (technical variants and improvements) :
Remarks (learning strategies) :
Chapter ML:IX IX. Deep Learning
Recurrent Neural Networks Notation I (MLP matrices)
Recurrent Neural Networks Notation I (MLP matrices)
Recurrent Neural Networks Notation I (MLP matrices)
Recurrent Neural Networks Notation I (MLP matrices)
Recurrent Neural Networks Notation I (MLP matrices)
Remarks:
Recurrent Neural Networks Types of Learning Tasks
Recurrent Neural Networks RNN Sequence Encoding
Recurrent Neural Networks (continued)
Recurrent Neural Networks (continued)
Recurrent Neural Networks (continued)
Remarks:
Recurrent Neural Networks RNN Sequence Encoding
Recurrent Neural Networks RNN Sequence Encoding
Recurrent Neural Networks RNN Sequence Encoding
Recurrent Neural Networks RNN Sequence Encoding
Recurrent Neural Networks RNN Sequence Encoding
Recurrent Neural Networks RNN Sequence Encoding
Recurrent Neural Networks RNN Sequence Encoding
Recurrent Neural Networks RNN Sequence Encoding
Recurrent Neural Networks RNN Sequence Encoding
Recurrent Neural Networks RNN Sequence Encoding
Recurrent Neural Networks RNN Sequence Encoding
Recurrent Neural Networks RNN Sequence Encoding
Recurrent Neural Networks RNN Sequence Encoding
Recurrent Neural Networks RNN Sequence Encoding
Recurrent Neural Networks RNN Sequence Encoding
Recurrent Neural Networks (S1) Sequence-to-Class: Sentiment Classification
Recurrent Neural Networks (S1) Sequence-to-Class: Sentiment Classification
Recurrent Neural Networks (S1) Sequence-to-Class: Sentiment Classification
Recurrent Neural Networks (S1) Sequence-to-Class Mapping with RNNs
Recurrent Neural Networks (S1) Sequence-to-Class Mapping with RNNs
Recurrent Neural Networks (S1) Sequence-to-Class Mapping with RNNs
Recurrent Neural Networks (S1) Sequence-to-Class Mapping with RNNs
Recurrent Neural Networks (S1) Sequence-to-Class Mapping with RNNs
Recurrent Neural Networks (S1) Sequence-to-Class Mapping with RNNs
Remarks:
Recurrent Neural Networks The IGD Algorithm for Sequence-to-Class Tasks
Recurrent Neural Networks The IGD Algorithm for Sequence-to-Class Tasks
Recurrent Neural Networks Types of Learning Tasks
Recurrent Neural Networks RNN Sequence Decoding
Recurrent Neural Networks (continued)
Recurrent Neural Networks (continued)
Recurrent Neural Networks (continued)
Remarks:
Recurrent Neural Networks RNN Sequence Decoding
Recurrent Neural Networks RNN Sequence Decoding
Recurrent Neural Networks RNN Sequence Decoding
Recurrent Neural Networks RNN Sequence Decoding
Recurrent Neural Networks RNN Sequence Decoding
Recurrent Neural Networks RNN Sequence Decoding
Recurrent Neural Networks RNN Sequence Decoding
Recurrent Neural Networks RNN Sequence Decoding
Recurrent Neural Networks RNN Sequence Decoding
Recurrent Neural Networks RNN Sequence Decoding
Recurrent Neural Networks RNN Sequence Decoding
Recurrent Neural Networks RNN Sequence Decoding
Recurrent Neural Networks RNN Sequence Decoding
Recurrent Neural Networks RNN Sequence Decoding
Recurrent Neural Networks RNN Sequence Decoding
Recurrent Neural Networks RNN Sequence Decoding
Recurrent Neural Networks RNN Sequence Decoding
Recurrent Neural Networks RNN Sequence Decoding
Recurrent Neural Networks RNN Sequence Decoding
Recurrent Neural Networks (S2) Class-to-Sequence: Text Generation
Recurrent Neural Networks (S2) Class-to-Sequence: Text Generation
Recurrent Neural Networks (S2) Class-to-Sequence: Text Generation
Recurrent Neural Networks (S2) Class-to-Sequence: Text Generation
Recurrent Neural Networks (S2) Class-to-Sequence: Text Generation
Recurrent Neural Networks (S2) Class-to-Sequence: Text Generation
Recurrent Neural Networks (S2) Class-to-Sequence: Text Generation
Recurrent Neural Networks (S2) Class-to-Sequence Mapping with RNNs
Recurrent Neural Networks (S2) Class-to-Sequence Mapping with RNNs
Recurrent Neural Networks (S2) Class-to-Sequence Mapping with RNNs
Recurrent Neural Networks (S2) Class-to-Sequence Mapping with RNNs
Recurrent Neural Networks (S2) Class-to-Sequence Mapping with RNNs
Recurrent Neural Networks (S2) Class-to-Sequence Mapping with RNNs
Recurrent Neural Networks (S2) Class-to-Sequence Mapping with RNNs
Recurrent Neural Networks (S2) Class-to-Sequence Mapping with RNNs
Recurrent Neural Networks (S2) Class-to-Sequence Mapping with RNNs
Remarks:
Recurrent Neural Networks The IGD Algorithm for Class-to-Sequence Tasks
Recurrent Neural Networks The IGD Algorithm for Class-to-Sequence Tasks
Remarks:
Chapter ML:IX IX. Deep Learning
RNNs for Machine Translation Statistical Machine Translation (SMT)
RNNs for Machine Translation Statistical Machine Translation (SMT)
RNNs for Machine Translation Statistical Machine Translation (SMT)
RNNs for Machine Translation Statistical Machine Translation (SMT)
RNNs for Machine Translation Statistical Machine Translation (SMT)
RNNs for Machine Translation Statistical Machine Translation (SMT)
Remarks:
RNNs for Machine Translation Statistical Machine Translation (SMT)
RNNs for Machine Translation Statistical Machine Translation (SMT)
RNNs for Machine Translation Statistical Machine Translation (SMT)
RNNs for Machine Translation Statistical Machine Translation (SMT)
Remarks (statistical machine translation) : Although p(y | x) can be maximized directly, Bayes rule is applied since the decomposition of
RNNs for Machine Translation Neural Machine Translation (NMT)
RNNs for Machine Translation Neural Machine Translation (NMT)
RNNs for Machine Translation Neural Machine Translation (NMT)
RNNs for Machine Translation Neural Machine Translation (NMT)
Remarks:
RNNs for Machine Translation Types of Learning Tasks
RNNs for Machine Translation (S3) Sequence-to-Sequence: Machine Translation
RNNs for Machine Translation (S3) Sequence-to-Sequence: Machine Translation
RNNs for Machine Translation (S3) Sequence-to-Sequence: Machine Translation
RNNs for Machine Translation (S3) Sequence-to-Sequence: Machine Translation
RNNs for Machine Translation (S3) Sequence-to-Sequence: Machine Translation
RNNs for Machine Translation (S3) Sequence-to-Sequence: Machine Translation
RNNs for Machine Translation (S3) Sequence-to-Sequence: Machine Translation
RNNs for Machine Translation (S3) Sequence-to-Sequence Mapping with RNNs
RNNs for Machine Translation (S3) Sequence-to-Sequence Mapping with RNNs
RNNs for Machine Translation (S3) Sequence-to-Sequence Mapping with RNNs
RNNs for Machine Translation (S3) Sequence-to-Sequence Mapping with RNNs
RNNs for Machine Translation (S3) Sequence-to-Sequence Mapping with RNNs
RNNs for Machine Translation (S3) Sequence-to-Sequence Mapping with RNNs
RNNs for Machine Translation (S3) Sequence-to-Sequence Mapping with RNNs
RNNs for Machine Translation (S3) Sequence-to-Sequence Mapping with RNNs
Remarks:
RNNs for Machine Translation Sequence-to-Sequence RNNs are Conditional Language Models
RNNs for Machine Translation Sequence-to-Sequence RNNs are Conditional Language Models
RNNs for Machine Translation Sequence-to-Sequence RNNs are Conditional Language Models
RNNs for Machine Translation Sequence-to-Sequence RNNs are Conditional Language Models
RNNs for Machine Translation Sequence-to-Sequence RNNs are Conditional Language Models
RNNs for Machine Translation Sequence-to-Sequence RNNs are Conditional Language Models
Remarks: Each output vector y(t) corresponds to a probability distribution over the Vocabularyd (recall
RNNs for Machine Translation Representation: Embeddings Instead of One-Hot Encoding
Kapitel ML:IX IX. Ensemble and Meta
Ensemble Methods Motivation (1): Generalisierungsfähigkeit
Ensemble Methods Motivation (2): No Free Lunch Theorem
Ensemble Methods Motivation (3): Instabilität von Lernverfahren
Ensemble Methods Motivation (4): Consequences
Ensemble Methods Bootstrap Aggregating (Bagging)
Remarks:
Ensemble Methods Bootstrap Aggregating
Ensemble Methods Bootstrap Aggregating
Ensemble Methods Bootstrap Aggregating
Ensemble Methods Boosting Weak Classifiers
Ensemble Methods Adaptive Boosting (AdaBoost)
Ensemble Methods Adaptive Boosting
Ensemble Methods Adaptive Boosting
Remarks:
Ensemble Methods Discrete AdaBoost
Ensemble Methods Cascades of Classifiers (Cascading)
Ensemble Methods Cascading
Ensemble Methods Maßzahlen für Cascading
Ensemble Methods Maßzahlen für Cascading
Ensemble Methods Cascading
Ensemble Methods Cascading for Face Detection (Main Loop)
Ensemble Methods Cascading for Face Detection (Gesamtklassifikator)
Ensemble Methods Cascading for Face Detection(Einzelklassifikator in Kaskade)
Ensemble Methods Cascading for Face Detection (Einzelklassifikator in Ensemble)
Ensemble Methods Cascading for Face Detection (alle Features)
Ensemble Methods Cascading for Face Detection (ausgewählte Features)
Ensemble Methods Cascading for Face Detection (Effizienz)
Ensemble Methods Cascading for Face Detection (Effizienz beim Training)
Ensemble Methods Trainingsmengen
Ensemble Methods Grundlage der Entscheidung
Ensemble Methods Literature