Index
A
B
C
D
E
F
G
H
I
J
K
L
M
- M5 algorithm / Regression model trees
- M5 model
- M5opt / Improvements to the M5 model
- machine learning / Machine learning or deep learning
- Major Atmospheric Gamma Imaging Cherenkov (MAGIC) / Limitations of boosting
- Mallow's Cp / Comparing different regression models
- margin / Maximal margin classification, Margins and out-of-bag observations
- Markov Chain Monte Carlo (MCMC) / Fitting an LDA model
- matrix energy / Singular value decomposition
- Matrix Market format / Modeling the topics of online news stories
- maximal margin classification / Maximal margin classification
- maximal margin hyperplane / Maximal margin classification
- McCulloch-Pitts model / The artificial neuron
- mean / Estimating the regression coefficients
- mean average error(MAE) / Evaluating individual predictions
- mean function / Generalized linear models
- mean squared error (MSE) / Evaluating individual predictions
- Mean Square Error (MSE) / Assessing regression models
- median / Residual analysis
- memory-based collaborative filtering / Collaborative filtering
- merely states / Predicting the sentiment of movie reviews
- meta parameter / Ridge regression
- Missing At Random (MAR) / Missing data
- Missing Completely At Random (MCAR) / Missing data
- Missing Not At Random (MNAR) / Missing data
- mixed selection / Feature selection
- MNIST database
- model-based collaborative filtering / Collaborative filtering
- model overfitting / Training and assessing the model
- models
- about / Models
- data, learning / Learning from data
- core components / The core components of a model
- k-nearest neighbors / Our first model – k-nearest neighbors
- types / Types of model
- supervised models / Supervised, unsupervised, semi-supervised, and reinforcement learning models
- unsupervised models / Supervised, unsupervised, semi-supervised, and reinforcement learning models
- semi-supervised models / Supervised, unsupervised, semi-supervised, and reinforcement learning models
- reinforcement learning models / Supervised, unsupervised, semi-supervised, and reinforcement learning models
- parametric models / Parametric and nonparametric models
- nonparametric models / Parametric and nonparametric models
- classification models / Regression and classification models
- regression models / Regression and classification models
- real-time machine learning models / Real-time and batch machine learning models
- batch machine learning models / Real-time and batch machine learning models
- training, at scale / Training models at scale
- pain, by phase / Pain by phase
- specific challenges / Specific challenges
- heterogeneity / Heterogeneity
- scale / Scale
- location / Location
- timeliness / Timeliness
- privacy / Privacy
- collaborations / Collaborations
- reproducibility / Reproducibility
- path forward / A path forward
- opportunities / Opportunities
- bigger data / Bigger data, bigger hardware
- bigger hardware / Bigger data, bigger hardware
- breaking up / Breaking up
- sampling / Sampling
- aggregation / Aggregation
- dimensional reduction / Dimensional reduction
- molecular biology
- morphological lemma / Predicting the sentiment of movie reviews
- multiclass classification
- multicollinearity / Multicollinearity
- multilayer perceptron (MLP) / Multilayer perceptron networks
- multilayer perceptron networks / Multilayer perceptron networks
- multinomial distribution / The Dirichlet distribution
- multinomial logistic regression / Multinomial logistic regression
- multiple linear regression / Introduction to linear regression
N
O
P
Q
R
S
T
U
V
W
Z
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