An introduction To Machine Learning

[hess]

سلام ..

یک دوره ی Course خیلی خوب و مختصر از مبحث Machine Learning برای آقای نیلسون بود؛ گفتم بذارم تا دوستان هم بتونن از اون استفاده کنن .. امیدوارم مطالب مفید باشن .. موفق باشید .. (فایل ها به صورت یک فایل RAR در انتهای این پست ضمیمه ی مجدد شدند)

[hess]

پاسخ : An introduction To Machine Learning

سلام ..

علاوه بر فایل های بالا؛ ریز سرفصل های این بحث رو در این پست میتونید ملاحظه کنید .. موفق باشید ..

Supervised Learning
Learning a Class from Examples
Vapnik-Chervonenkis (VC) Dimension
Probably Approximately Correct (PAC) Learning
Noise
Learning Multiple Classes
Regression
Model Selection and Generalization
Dimensions of a Supervised Machine Learning Algorithm

Bayesian Decision Theory
Classification
Losses and Risks
Discriminant Functions
Utility Theory
Value of Information
Bayesian Networks
Influence Diagrams
Association Rules

Parametric Methods
Maximum Likelihood Estimation
Bernoulli Density
Multinomial Density
Gaussian (Normal) Density
Evaluating an Estimator: Bias and Variance
The Bayes' Estimator
Parametric Classification
Regression
Tuning Model Complexity: Bias/Variance Dilemma
Model Selection Procedures

Multivariate Methods
Multivariate Data
Parameter Estimation
Estimation of Missing Values
Multivariate Normal Distribution
Multivariate Classification
Tuning Complexity
Discrete Features
Multivariate Regression

Dimensionality Reduction
Subset Selection
Principal Components Analysis
Factor Analysis
Multidimensional Scaling
Linear Discriminant Analysis

Clustering
Mixture Densities
K-Means Clustering
Expectation-Maximization Algorithm
Mixtures of Latent Variable Models
Supervised Learning after Clustering
Hierarchical Clustering
Choosing the Number of Clusters

Nonparametric Methods
Nonparametric Density Estimation
Histogram Estimator
Kernel Estimator
K-Nearest Neighbor Estimator
Generalization to Multivariate Data
Nonparametric Classification
Condensed Nearest Neighbor
Nonparametric Regression: Smoothing Models
Running Mean Smoother
Kernel Smoother
Running Line Smoother
How to Choose the Smoothing Parameter

Decision Trees
Univariate Trees
Classification Trees
Regression Trees
Pruning
Rule Extraction from Trees
Learning Rules from Data
Multivariate Trees

Linear Discrimination
Generalizing the Linear Model
Geometry of the Linear Discriminant
Two Classes
Multiple Classes
Pairwise Separation
Parametric Discrimination Revisited
Gradient Descent
Logistic Discrimination
Two Classes
Multiple Classes
Discrimination by Regression
Support Vector Machines
Optimal Separating Hyperplane
The Nonseparable Case: Soft Margin Hyperplane
Kernel Functions
Support Vector Machines for Regression

Multilayer Perceptrons
Understanding the Brain
Neural Networks as a Paradigm for Parallel Processing
The Perceptron
Training a Perceptron
Learning Boolean Functions
Multilayer Perceptrons
MLP as a Universal Approximator
Backpropagation Algorithm
Nonlinear Regression
Two-Class Discrimination
Multiclass Discrimination
Multiple Hidden Layers
Training Procedures
Improving Convergence
Momentum
Adaptive Learning Rate
Overtraining
Structuring the Network
Hints
Tuning the Network Size
Bayesian View of Learning
Dimensionality Reduction
Learning Time
Time Delay Neural Networks
Recurrent Networks

Local Models
Competitive Learning
Online k-Means
Adaptive Resonance Theory
Self-Organizing Maps
Radial Basis Functions
Incorporating Rule-Based Knowledge
Normalized Basis Functions
Competitive Basis Functions
Learning Vector Quantization
Mixture of Experts
Cooperative Experts
Competitive Experts
Hierarchical Mixture of Experts

Hidden Markov Models
Discrete Markov Processes
Hidden Markov Models
Three Basic Problems of HMMs
Evaluation Problem
Finding the State Sequence
Learning Model Parameters
Continuous Observations
The HMM with Input
Model Selection in HMM

Assessing and Comparing Classification Algorithms
Cross-Validation and Resampling Methods
K-Fold Cross-Validation
5x2 Cross-Validation
Bootstrapping
Measuring Error
Interval Estimation
Hypothesis Testing
Assessing a Classification Algorithm's Performance
Binomial Test
Approximate Normal Test
Paired t Test
Comparing Two Classification Algorithms
McNemar's Test
K-Fold Cross-Validated Paired t Test
5x2 cv Paired t Test
5x2 cv Paired F Test
Comparing Multiple Classification Algorithms: Analysis of Variance

Combining Multiple Learners
Voting
Error-Correcting Output Codes
Bagging
Boosting
Mixture of Experts Revisited
Stacked Generalization
Cascading

Reinforcement Learning
Single State Case: K-Armed Bandit
Elements of Reinforcement Learning
Model-Based Learning
Value Iteration
Policy Iteration
Temporal Difference Learning
Exploration Strategies
Deterministic Rewards and Actions
Nondeterministic Rewards and Actions
Eligibility Traces
Generalization
Partially Observable States

[hess]

Pattern Recognition And Machine Learning

سلام ..

فایلی که در این پست ضمیمه شده، نسخه ی کلاسیک؛ منبع کامل و مرجع Machine Learning and Pattern Recognition هست که در زمینه های مختلف هوش مصنوعی مورد استفاده و استقبال زیادی قرار میگیره .. به دلیل حجم زیاد، فایل رو در دو قسمت پلود کردم .. برای استفاده از فایل PDF ابتدا دو فایل RAR رو دانلود کنید و سپس یکی از اونها رو باز کنید .. (نکته ی کوتاه: فصل 12ام کتاب متاسفانه حذف شده از متن [علتش رو نمیدونم]) .. امیدوارم که این کتاب بتونه براتون مفید باشه .. موفق و سلامت و شاد باشید ..