Linear regression is introduced as the foundational supervised learning algorithm for predicting continuous numeric values, using cost estimation of Portland houses as an example. The episode explains the three-step process of machine learning - prediction via a hypothesis function, error calculation with a cost function (mean squared error), and parameter optimization through gradient descent - and details both the univariate linear regression model and its extension to multiple features.

Links

• Notes and resources at ocdevel.com/mlg/5
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Linear Regression Overview of Machine Learning Structure

• Machine learning is a branch of artificial intelligence, alongside statistics, operations research, and control theory.
• Within machine learning, supervised learning involves training with labeled examples and is further divided into classification (predicting discrete classes) and regression (predicting continuous values).

Linear Regression and Problem Framing

• Linear regression is the simplest and most commonly taught supervised learning algorithm for regression problems, where the goal is to predict a continuous number from input features.
• The episode example focuses on predicting the cost of houses in Portland, using square footage and possibly other features as inputs.

The Three Steps of Machine Learning in Linear Regression

• Machine learning in the context of linear regression follows a standard three-step loop: make a prediction, measure how far off the prediction is, and update the prediction method to reduce mistakes.
• Predicting uses a hypothesis function (also called objective or estimate) that maps input features to a predicted value.

The Hypothesis Function

• The hypothesis function is a formula that multiplies input features by coefficients (weights) and sums them to make a prediction; in mathematical terms, for one feature, it is: h(x) = theta_1 * x_1 + theta_0
  • Here, theta_1 is the weight for the feature (e.g., square footage), and theta_0 is the bias (an average baseline).
• With only one feature, the model tries to fit a straight line to a scatterplot of the input feature versus the actual target value.

Bias and Multiple Features

• The bias term acts as the starting value when all features are zero, representing an average baseline cost.
• In practice, using only one feature limits accuracy; including more features (like number of bedrooms, bathrooms, location) results in multivariate linear regression: h(x) = theta_0 + theta_1 * x_1 + theta_2 * x_2 + ... for each feature x_n.

Visualization and Model Fitting

• Visualizing the problem involves plotting data points in a scatterplot: feature values on the x-axis, actual prices on the y-axis.
• The goal is to find the line (in the univariate case) that best fits the data, ideally passing through the "center" of the data cloud.

The Cost Function (Mean Squared Error)

• The cost function, or mean squared error (MSE), measures model performance by averaging squared differences between predictions and actual labels across all training examples.
• Squaring ensures positive and negative errors do not cancel each other, and dividing by twice the number of examples (2m) simplifies the calculus in the next step.

Parameter Learning via Gradient Descent

• Gradient descent is an iterative algorithm that uses calculus (specifically derivatives) to find the best values for the coefficients (thetas) by minimizing the cost function.
• The cost function’s surface can be imagined as a bowl in three dimensions, where each point represents a set of parameter values and the height represents the error.
• The algorithm computes the slope at the current set of parameters and takes a proportional step (controlled by the learning rate alpha) toward the direction of the steepest decrease.
• This process is repeated until reaching the lowest point in the bowl, where error is minimized and the model best fits the data.
• Training will not produce a perfect zero error in practice, but it will yield the lowest achievable average error for the data given.

Extension to Multiple Variables

• Multivariate linear regression extends all concepts above to datasets with multiple input features, with the same process for making predictions, measuring error, and performing gradient descent.
• Technical details are essentially the same though visualization becomes complex as the number of features grows.

Essential Learning Resources

• The episode strongly directs listeners to the Andrew Ng course on Coursera as the primary recommended starting point for studying machine learning and gaining practical experience with linear regression and related concepts.