Machine Learning Session 1

Machine Learning Session 1

• Machine Learning is a branch of Artificial Intelligence focused on building systems that learn from data instead of explicit programming. ML models identify patterns and learn from the input data.

Agenda

• The agenda for the session covers:
  • Introduction to Machine Learning
  • Linear Regression
  • Polynomial Regression

Introduction to Machine Learning

• Machine Learning is a subset of Artificial Intelligence (AI) that focuses on enabling computer systems to learn from data without explicit programming for a particular task.

Types of Machine Learning

• Supervised Learning: Models are trained using labeled data (input-output pairs). Examples include:

  • Classification (e.g., spam detection)
  • Regression (e.g., predicting house prices).

• Unsupervised Learning: Models learn from unlabeled data by identifying patterns and inherent structures. Examples include:

  • Clustering (e.g., customer segmentation)
  • Association (e.g., market basket analysis)

• Reinforcement Learning: Models learn through interaction with an environment, receiving rewards or penalties for actions. Examples include teaching robots to navigate mazes.

Supervised vs. Unsupervised Learning

• Supervised: Labeled data (input-output pairs) are used to train the model. A target variable (Y) exists.

• Unsupervised: Unlabeled data is used to train the model. No target variable (Y) exists.

Types of Machine Learning (Detailed)

• Regression: Aims to predict continuous numerical values by fitting a line or curve to the data. Examples include predicting house prices.

• Classification: Aims to categorize data into discrete classes by applying a boundary. Examples include identifying spam emails.

Key Differences

• Classification: The goal involves dividing data into different categories using a boundary. (e.g., Identifying spam emails from non-spam emails)

• Regression: The objective is to predict continuous numerical values by fitting a line or curve to the data. (e.g., Predicting house prices).

Linear Regression

• Linear regression is a common predictive modeling technique used to model the relationship between one dependent variable and one or more independent variables. It fits a linear equation to observed data.

Simple Linear Regression

• Simple linear Regression uses one independent variable to predict a dependent variable.
• The relationship is modeled by a straight line:

  y = β₀ + β₁x + ε
  

  Where:
  • y is the dependent variable
  • x is the independent variable
  • β₀ is the y-intercept
  • β₁ is the slope coefficient
  • ε is the error term

Multiple Linear Regression

• Multiple linear regression extends simple linear regression by using two or more independent variables to predict one dependent variable.

   y = β₀ + β₁X₁ + β₂X₂ + ... + βₙXₙ + ε
  

  Where:
  • y is the dependent variable
  • X₁, X₂, ... Xₙ are the independent variables
  • β₀ is the y-intercept
  • β₁, β₂, ... βₙ are the coefficients for the respective independent variables
  • ε is the error term

Polynomial Regression

• Polynomial regression models the relationship between the independent variable (x) and the dependent variable (y) as an n-degree polynomial. Unlike linear regression, it fits a curve to the data.

   y = β₀ + β₁x + β₂x² + ... + βₙxⁿ + ε
  

  Where:
  • y is the dependent variable
  • x is the independent variable
  • β₀, β₁, β₂, ... βₙ are the coefficients
  • n is the degree of the polynomial
  • ε is the error term

Key Characteristics of Polynomial Models

• Polynomial models can capture non-linear relationships, improving accuracy over models that only fit straight lines to data.

Cost Function

• The cost function (J(θ₀, θ₁)) measures the performance of a linear regression model by evaluating the difference between predicted values (hθ(x)) and actual values (y) using the Mean Squared Error (MSE) formula.

Mean Squared Error (MSE)

J(θ₀, θ₁) = 1/2m ∑(hθ(xᵢ) - yᵢ)²

Root Mean Squared Error (RMSE)

• RMSE is calculated by taking the square root of the MSE. Provides error in the same units as the original data

Mean Absolute Error (MAE)

• MAE measures the average absolute difference between the predicted and actual values. Less sensitive to outliers than MSE.

Gradient Descent

• Gradient descent is an optimization algorithm used to minimize the cost function (e.g., MSE) in a linear regression model by iteratively adjusting the model's parameters in the direction of the steepest descent of the cost function.

Parameter Update Rules

• Parameters are adjusted using specific rules based on the partial derivatives of the cost function with respect to each parameter, using the learning rate (α) to control the size of steps. 

  θ₀ = θ₀ - α * ∂J/∂θ₀
  θ₁ = θ₁ - α * ∂J/∂θ₁
  

Gradient Descent Iterative Process

• The objective of Gradient Descent is to find the global minimum of the cost function through iterative parameter updates.
• The learning rate (α) affects the convergence speed of the algorithm and can either cause instability or slow convergence if it is too large or too small, respectively.

Explore the foundations of Machine Learning in this session. Learn about supervised and unsupervised learning techniques, including linear and polynomial regression. This quiz will test your understanding of essential concepts in this exciting field.