# 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  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

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