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Премиум
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    [Important] Getting the most out of this course
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    About using MATLAB or Python
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    Statistics guessing game!
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    Using the Q&A forum
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    (optional) Entering time-stamped notes in the Udemy video player
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    Should you memorize statistical formulas?
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    Arithmetic and exponents
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    Scientific notation
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    Summation notation
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    Absolute value
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    Natural exponent and logarithm
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    The logistic function
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    Rank and tied-rank
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    Download materials for the entire course!
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    Is "data" singular or plural?!?!!?!
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    Where do data come from and what do they mean?
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    Types of data: categorical, numerical, etc
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    Code: representing types of data on computers
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    Sample vs. population data
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    Samples, case reports, and anecdotes
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    The ethics of making up data
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    Bar plots
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    Code: bar plots
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    Box-and-whisker plots
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    Code: box plots
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    "Unsupervised learning": Boxplots of normal and uniform noise
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    Histograms
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    Code: histograms
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    "Unsupervised learning": Histogram proportion
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    Pie charts
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    Code: pie charts
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    When to use lines instead of bars
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    Linear vs. logarithmic axis scaling
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    Code: line plots
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    "Unsupervised learning": log-scaled plots
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    Descriptive vs. inferential statistics
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    Accuracy, precision, resolution
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    Data distributions
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    Code: data from different distributions
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    "Unsupervised learning": histograms of distributions
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    The beauty and simplicity of Normal
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    Measures of central tendency (mean)
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    Measures of central tendency (median, mode)
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    Code: computing central tendency
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    "Unsupervised learning": central tendencies with outliers
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    Measures of dispersion (variance, standard deviation)
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    Code: Computing dispersion
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    Interquartile range (IQR)
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    Code: IQR
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    QQ plots
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    Code: QQ plots
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    Statistical "moments"
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    Histograms part 2: Number of bins
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    Code: Histogram bins
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    Violin plots
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    Code: violin plots
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    "Unsupervised learning": asymmetric violin plots
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    Shannon entropy
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    Code: entropy
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    "Unsupervised learning": entropy and number of bins
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    Garbage in, garbage out (GIGO)
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    Z-score standardization
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    Code: z-score
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    Min-max scaling
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    Code: min-max scaling
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    "Unsupervised learning": Invert the min-max scaling
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    What are outliers and why are they dangerous?
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    Removing outliers: z-score method
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    The modified z-score method
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    Code: z-score for outlier removal
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    "Unsupervised learning": z vs. modified-z
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    Multivariate outlier detection
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    Code: Euclidean distance for outlier removal
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    Removing outliers by data trimming
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    Code: Data trimming to remove outliers
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    Non-parametric solutions to outliers
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    An outlier lecture on personal accountability
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    What is probability?
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    Probability vs. proportion
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    Computing probabilities
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    Code: compute probabilities
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    Probability and odds
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    "Unsupervised learning": probabilities of odds-space
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    Probability mass vs. density
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    Code: compute probability mass functions
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    Cumulative probability distributions
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    Code: cdfs and pdfs
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    "Unsupervised learning": cdf's for various distributions
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    Creating sample estimate distributions
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    Monte Carlo sampling
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    Sampling variability, noise, and other annoyances
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    Code: sampling variability
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    Expected value
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    Conditional probability
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    Code: conditional probabilities
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    Tree diagrams for conditional probabilities
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    The Law of Large Numbers
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    Code: Law of Large Numbers in action
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    The Central Limit Theorem
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    Code: the CLT in action
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    "Unsupervised learning": Averaging pairs of numbers
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    IVs, DVs, models, and other stats lingo
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    What is an hypothesis and how do you specify one?
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    Sample distributions under null and alternative hypotheses
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    P-values: definition, tails, and misinterpretations
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    P-z combinations that you should memorize
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    Degrees of freedom
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    Type 1 and Type 2 errors
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    Parametric vs. non-parametric tests
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    Multiple comparisons and Bonferroni correction
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    Statistical vs. theoretical vs. clinical significance
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    Cross-validation
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    Statistical significance vs. classification accuracy
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    Purpose and interpretation of the t-test
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    One-sample t-test
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    Code: One-sample t-test
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    "Unsupervised learning": The role of variance
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    Two-samples t-test
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    Code: Two-samples t-test
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    "Unsupervised learning": Importance of N for t-test
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    Wilcoxon signed-rank (nonparametric t-test)
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    Code: Signed-rank test
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    Mann-Whitney U test (nonparametric t-test)
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    Code: Mann-Whitney U test
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    Permutation testing for t-test significance
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    Code: permutation testing
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    "Unsupervised learning": How many permutations?
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    What are confidence intervals and why do we need them?
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    Computing confidence intervals via formula
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    Code: compute confidence intervals by formula
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    Confidence intervals via bootstrapping (resampling)
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    Code: bootstrapping confidence intervals
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    "Unsupervised learning:" Confidence intervals for variance
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    Misconceptions about confidence intervals
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    Motivation and description of correlation
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    Covariance and correlation: formulas
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    Code: correlation coefficient
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    Code: Simulate data with specified correlation
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    Correlation matrix
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    Code: correlation matrix
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    "Unsupervised learning": average correlation matrices
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    "Unsupervised learning": correlation to covariance matrix
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    Partial correlation
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    Code: partial correlation
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    The problem with Pearson
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    Nonparametric correlation: Spearman rank
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    Fisher-Z transformation for correlations
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    Code: Spearman correlation and Fisher-Z
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    "Unsupervised learning": Spearman correlation
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    "Unsupervised learning": confidence interval on correlation
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    Kendall's correlation for ordinal data
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    Code: Kendall correlation
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    "Unsupervised learning": Does Kendall vs. Pearson matter?
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    The subgroups correlation paradox
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    Cosine similarity
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    Code: Cosine similarity vs. Pearson correlation
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    ANOVA intro, part1
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    ANOVA intro, part 2
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    Sum of squares
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    The F-test and the ANOVA table
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    The omnibus F-test and post-hoc comparisons
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    The two-way ANOVA
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    One-way ANOVA example
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    Code: One-way ANOVA (independent samples)
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    Code: One-way repeated-measures ANOVA
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    Two-way ANOVA example
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    Code: Two-way mixed ANOVA
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    Introduction to GLM / regression
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    Least-squares solution to the GLM
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    Evaluating regression models: R2 and F
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    Simple regression
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    Code: simple regression
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    "Unsupervised learning": Compute R2 and F
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    Multiple regression
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    Standardizing regression coefficients
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    Code: Multiple regression
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    Polynomial regression models
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    Code: polynomial modeling
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    "Unsupervised learning": Polynomial design matrix
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    Logistic regression
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    Code: Logistic regression
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    Under- and over-fitting
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    "Unsupervised learning": Overfit data
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    Comparing "nested" models
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    What to do about missing data
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    What is statistical power and why is it important?
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    Estimating statistical power and sample size
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    Compute power and sample size using G*Power
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    K-means clustering
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    Code: k-means clustering
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    "Unsupervised learning:" K-means and normalization
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    "Unsupervised learning:" K-means on a Gauss blur
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    Clustering via dbscan
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    Code: dbscan
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    "Unsupervised learning": dbscan vs. k-means
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    K-nearest neighbor classification
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    Code: KNN
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    Principal components analysis (PCA)
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    Code: PCA
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    "Unsupervised learning:" K-means on PC data
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    Independent components analysis (ICA)
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    Code: ICA
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    The two perspectives of the world
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    d-prime
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    Code: d-prime
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    Response bias
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    Code: Response bias
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    Receiver operating characteristics (ROC)
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    Code: ROC curves
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    "Unsupervised learning": Make this plot look nicer!