Index
A
- A/B testing for evaluation, Evaluation, A/B Tests-A/B Tests
- absolute timestamps, Thought Experiment
- abuse of notation, A Real-World Recommendation Engine
- access to medical records thought experiment, Thought Experiment
- accuracy, Pick an evaluation metric, How About k-nearest Neighbors?, Evaluation
- accuracy of models, Accuracy: Meh
- actors, Terminology from Social Networks
- actual errors, Adding in modeling assumptions about the errors
- actual preferences, Principal Component Analysis (PCA)
- adding predictors, Adding other predictors
- additive estimates, Exponential Downweighting
- adjacency matrix, Representations of Networks and Eigenvalue Centrality
- adversarial behavior, Challenges in features and learning
- aggregate users, Exploratory Data Analysis (EDA)
- aggregating bootstraps, Random Forests
- Akaike Information Criterion (AIC), Selection criterion
- Aldhous, Peter, Thought Experiment: What Are the Ethical Implications of a Robo-Grader?
- algorithms, Algorithms
- association, Being an Ethical Data Scientist
- at scale, Sample R code: K-NN on the housing dataset
- Basic Machine Learning Algorithm Exercise, Exercise: Basic Machine Learning Algorithms-Sample R code: K-NN on the housing dataset
- black box, Machine Learning Algorithms
- clustering, Thought Experiment: Meta-Definition, k-means
- constraints on, Classifiers-Scalability
- converged, Alternating Least Squares
- evaluating models and, Choosing an Algorithm
- for wrapper feature selection, Selecting an algorithm
- Fruchterman-Reingold, Morningside Analytics
- k-means, k-means ...
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