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Machine Learning:

Regression

Week 1

Simple Linear Regression

(080616)

  • Regression fundamentals

  • The simple linear regression model, its use, and interpretation

  • An aside on optimization: one dimensional objectives

  • An aside on optimization: multidimensional objectives

  • Finding the least squares line

  • Discussion and summary of simple linear regression

  • Programming assignment

  • Quiz: Simple Linear Regression

  • Quiz: Fitting a simple linear regression model on housing data

Week 2

Multiple Regression

(080616)

  • Multiple features of one input

  • Incorporating multiple inputs

  • Setting the stage for computing the least squares fit

  • Computing the least squares D-dimensional curve

  • Summarizing multiple regression

  • Programming assignment 1

  • Programming assignment 2

  • Quiz: Multiple Regression

  • Quiz: Exploring different multiple regression models for house price prediction

  • Quiz: Implementing gradient descent for multiple regression

Week 3

Assessing Performance

(080616)

  • Defining how we assess performance

  • 3 measures of loss and their trends with model complexity

  • 3 sources of error and the bias-variance tradeoff

  • OPTIONAL ADVANCED MATERIAL:

         Formally defining and deriving the 3 sources of error

  • Putting the pieces together

  • Programming assignment

  • Quiz: Assessing Performance

  • Quiz: Exploring the bias-variance tradeoff

Week 4

Ridge Regression

(080616)

  • Characteristics of overfit models

  • The ridge objective

  • Optimizing the ridge objective

  • Tying up the loose ends

  • Programming Assignment 1

  • Programming Assignment 2

  • Quiz: Ridge Regression

  • Quiz: Observing effects of L2 penalty in polynomial regression

  • Quiz: Implementing ridge regression via gradient descent

Week 5

Feature Selection & Lasso

(080616)

  • Feature selection via explicit model enumeration

  • Feature selection implicitly via regularized regression

  • Geometric intuition for sparsity of lasso solutions

  • Setting the stage for solving the lasso

  • Optimizing the lasso objective

  • OPTIONAL ADVANCED MATERIAL:

       Deriving the lasso coordinate descent update

Week 5

Feature Selection & Lasso

(080616)

  • Tying up loose ends

  • Programming Assignment 1

  • Programming Assignment 2

  • Quiz: Feature Selection and Lasso

  • Quiz: Using LASSO to select features

  • Quiz: Implementing LASSO using coordinate descent

Week 6

Nearest Neighbors & Kernel Regression

Closing Remarks

(080616)

  • Motivating local fits

  • Nearest neighbor regression

  • k-Nearest neighbors and weighted k-nearest neighbors

  • Kernel regression

  • k-NN and kernel regression wrapup

  • Programming Assignment

  • What we've learned

  • Summary and what's ahead in the specialization

  • Quiz: Nearest Neighbors & Kernel Regression

  • Quiz: Predicting house prices using k-nearest neighbors regression

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