**Digital Transformation or Disruption? – DSPs, Digital Advertising, and the Changing Role of the Agency**

# Category Archives: Machine Learning

# Современные достижения и перспективы Робототехники и Искусственного Интеллекта

Директор лаборатории Искусственного Интеллекта Стенфорда Эндрю Энг рассказывает о современных достижениях и перспективах Искусственного Интеллекта. Значительную часть доклада он посвещает искусственным нейронным сетям.

# Machine Learning Algorithms

Supervised Learning

Linear Regression (Hypothesis, Uni-variable, Cost-function, Gradient Descent, Multivariable, Feature Scaling, Mean normalization)

Polynomial Regression

Normal Equation (Algorithm, Comparison with Gradient Descent, Case with Non-invertability matrix )

Logistic Regression for Classification (Hypothesis, Non-linear decision boundaries, Cost-function, Simplified cost-function and Gradient Descent)

Advanced Optimization

Advanced Algorithms (Conjugate Gradient, BFGS, L-BFGS)

Multi-class classification: One-vs-all

Regularization: Over-fitting problem, Cost-function, Linear Regression, Normal Equation, Logistic Regression, Advanced Algs)

Take away from AI-Class

Applications

Supervised Learning

OCCAM’s RAZOR

Spam Detection (with Naive Bayes)

Maximum Likelihood

Laplace Smoothing

Advanced SPAM filtering

Handwriting classification

Overfitting prevetion

Linear Regression

Regularization

Perceptron Algorithm

Maximum Margin Algorithms (SVM, Boosting)

K-nearest neighbors (Algorithm, Problems)

Unsupervised Learning

Density estimation, Dimensionality Reduction, Blind sequence separation, Factor Analysis

Clustering

k-means (algorithm, problems)

Expectation minimization

Gaussians and Normal Distribution

Gaussian Learning

Algorithm

# of cluster calculation

Dimensionality Reduction

Spectral Clustering

# Machine Learning Tools

Octave

Mathlab

Reddis (Large scale learning)

# Machine Learning Intro

Machine Learning is **the instrument to “learn” models from data** (i.e. *inferring Bayes Networks from the available data ). *Machine Learning algorithms/approaches could be identified by following aspects.

**How they defined:** Parameters, Structure, Hidden concepts

**How they learn:** Supervised, Unsupervised, Reinforcement learning

**What they are used for:** Prediction, Diagnostics, Summarization, etc.

**How learning is done:** Passive, Active, Online, Offline.

**Outputs:** Regression Vs. Classification