Machine Learning Week6 Advice for Applying Machine Learning

Evaluating a Learning Algorithm

Deciding what to try next

  • We know many learning algorithm
    • But,how to choose the best algorithm to explore the various techniques
    • Here we focus deciding what avenues to try

Machine Learning Week5 Neural Networks Learning

Cost Function and Backpropagation

Cost Function

  • L = total number of layers in the network
  • sls = number of units (not counting bias unit) in layer l
  • K = number of output units/classes

Machine Learning Week4 Neural Networks Representation

Motivations

Non-linear Hypotheses

Why do we need neural networks?

  • Consider a supervised learning classification problem
    • logistic regression
    • g as usual is sigmoid function
    • And, if you include enough polynomial terms then, you know, maybe you can get a hypotheses.
    • However, this problem is just about two features x1 and x2, many machine learning problems would have a lot more features.
  • e.g. our housing example
    • 100 house features, predict odds of a house being sold in the next 6 months
    • Here, if you included all the quadratic terms (second order)
      • There are lots of them (x12 ,x1x2, x1x4 ..., x1x100)
      • For the case of n = 100, you have about 5000 features
      • Number of features grows O(n2)
  • Not a good way to build classifiers when n is large

Machine Learning Week3 Logistic Regression

Classification and Representation

Classification

  • y is a discrete value

  • Variable in these problems is Y

    • Y is either 0 or 1
      • 0 = negative class (absence of something)
      • 1 = positive class (presence of something)
    • Start with binary class problems
      • Later look at multiclass classification problem

Machine Learning Week2 Linear Regression with Multiple Variables

Multivariate Linear Regression

Multiple Features

  • Multiple variables = multiple features
  • If in a new scheme we have more features to predict the price of the house
    • x1, x2, x3, x4 are the four features
      • x1 - size (feet squared)
      • x2 - Number of bedrooms
      • x3 - Number of floors
      • x4 - Age of home (years)
    • y is the output variable (price)

Machine Learning Week1 Introduction

Introduction

What is machine learning?

  • Two definitions:
    • Arthur Samuel(1959)
      • "The field of study that gives computers the ability to learn without being explicitly programmed."
      • This is an older, informal definition.
    • Tom Mitchell(1999)
      • "A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P, if its performance at tasks in T, as measured by P, improves with experience E."

        The checkers example.
        • E = the experience of playing many games of checkers
        • T = the task of playing checkers.
        • P = the probability that the program will win the next game.
  • Several types of learning algorithms
    • Supervised learning
    • Unsupervised learning
    • Reinforcement learning
    • Recommender systems
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