Hypothesis Testing and Classification

  • 17 Videos | 1h 50m 32s
  • Includes Assessment
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This course will cover the basics of anomaly detection and classification: for these tasks there are methods coming from either statistics or machine learning that are built on different principles. As well as the fundamentals of hypothesis testing, which is the formalization of scientific inquiry. This delicate statistical setup obeys a certain set of rules that will be explained and put in context with classification.

WHAT YOU WILL LEARN

  • Understand what classification is
    Know what binary classification is
    Learn more about binary classification
    Know how to evaluate a classifier and understand the metrics used to do so
    Know what hypothesis testing is
    Understand what a confidence interval is
    Understand hypothesis testing when the distribution is binomial
    Know how to use the statistics obtained in an appropriate manner
  • Be able to apply the p-value to your calculation
    Know how to estimate likelihood
    Know how to use non-statistical classifiers
    Understand the perceptron algorithm
    Understand the proof behind the perceptron algorithm
    Be able to use SVMs for more complicated problems
    Be able to use the methods for estimating the parameters of logistic regression
    Know how statistical techniques could be misapplied

IN THIS COURSE

  • Playable
    1. 
    Introduction
    1m 5s
    UP NEXT
  • Playable
    2. 
    What Are Anomalies? What Is Fraud? Spams? Part 1
    1m 51s
  • Locked
    3. 
    What Are Anomalies? What Is Fraud? Spams? Part 2
    3m 28s
  • Locked
    4. 
    What Are Anomalies? What is Fraud? Spams? Part 3
    5m 53s
  • Locked
    5. 
    False Positive/Negative, Precision/Recall, F-Score
    7m 13s
  • Locked
    6. 
    Hypothesis Testing
    1m 20s
  • Locked
    7. 
    Confidence Intervals
    6m 46s
  • Locked
    8. 
    Validity Of Binomial Distribution
    5m 45s
  • Locked
    9. 
    Misuses Of Statistics
    9m 6s
  • Locked
    10. 
    P-Value
    9m 39s
  • Locked
    11. 
    Methods Of Estimating Likelihood
    6m 42s
  • Locked
    12. 
    Support Vector Machine: Non-Statistical Classifier
    8m 33s
  • Locked
    13. 
    Perceptron
    10m 27s
  • Locked
    14. 
    Perceptron Proof
    8m 10s
  • Locked
    15. 
    Perceptron & Data That Is Not Linearly Separable
    10m 16s
  • Locked
    16. 
    Estimating The Parameters Of Logistic Regression
    7m 49s
  • Locked
    17. 
    Misapplications Of Statistical Techniques
    6m 27s