Learning Topics

  1. Linear regression
  2. Central methods in ML
  3. The concept of classification problem through graphical representation
  4. Objective of classification problem

Key Concepts

Regression and Machine Learning Fundamentals

  • Regression is the cornerstone of ML and DS (Data Science)
  • Central methods in machine learning focus on supervised learning
  • We use labeled datasets to train algorithms to predict accurate outcomes

Linear Regression

  • Purpose: Predict the value of an unobserved variable (linear regression)
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Process of Linear Regression

  1. Given datapoints with known (x,y) values (labeled examples)
  2. When a new person provides their x value
  3. Make a prediction of their y value by:
    • Drawing a line
    • Reading the predicted y value from the line

Classification Problems

  1. Objective: Predict the type of an individual
  2. Characteristics:
    • Individuals have attributes (X1, X2)
    • Outcome could be binary (e.g., blue or brown)
    • Given examples of people with known attributes and classifications
    • Goal: Create a classification rule by dividing X space into parts

Model Evaluation

  • Focus on the critical question: How good is our method, model, and predictions?
  • Requires systematic Testing and Validation

Key Points about Supervised Learning

  • Uses labeled examples for training
  • Aims to generalize from known examples
  • Linear Regression specifically:
    • Predicts one variable's value based on another
    • Can be generalized to higher dimensions