/pandoc-gfm

Howto pandoc markdown with latex contents in github project, especially serving it as README.md

Primary LanguageHTML

Howto pandoc markdown with latex contents in github project, especially serving it as README.md

  • Write README.pandoc.md

  • Publish as README.md (github flavored markdown + webtex)

  • Use following command to convert (incorporate to build script or commit hook will be nice)

    pandoc -t markdown_github --webtex README.pandoc.md -o README.md

test document follows

Machine Learning Lecture by Andrew Ng on Coursera

Week 1

Introduction

  • What is Machine Learning?
  • Supervised Learning
  • Unsupervised Learning

Linear Regression with One Variable

  • Model (Hypothesis)
  • Cost Function
  • Gradient Descent

Week 2

Linear Regression with Multiple Variable

  • Multiple Features
  • Feature Scaling
  • Learning Rate
  • Features and Polymomial Regression
  • Computing Parameters Analytically - Normal Equation

Week 3

Logistic Regression

  • Classification
  • Logistic Hypothesis
  • Decision Boundary
  • Cost Function
  • Simplified Cost Function and Gradient Descent
  • Advanced Optimization
  • Multiclass Classification - One-vs-all

Regularization

  • The Problem of Overfitting
  • Cost Function
  • Regularized Linear Regression

Week 4

Neural Networks: Representation

  • Non-linear Hypothesis
  • Neurons and the Brain
  • Model Representation
    x = a^{(1)} \to g(a^{(1)} * \Theta^{(1)}) = a^{(2)} \to g(a^{(2)} * \Theta^{(2)}) = a^{(3)} \to ... \to g(a^{(l-1)} * \Theta^{(l-1)}) = a^{(l)} = h_\Theta(x)
  • Example - AND, ! AND !, OR -> XOR
  • Example - Multclass Classification

Week 5

Neural Networks: Learning

  • Cost Function - NN multiclass classification
  • Backpropagation

Backpropagation in Practice

  • Unrolling Parameters
  • Gradient Checking
  • Random Initialization
  • Putting It Together

Week 6

Advice for Applying Machine Learning

  • Not-satisfiying result! -> What to Try Next?
    • More training examples
    • Trying smaller sets of features
    • Trying additional sets of features
    • Trying polynomial features
    • Increasing or decreasing \lambda

Training set / Cross Validation set / Test set

  • Evaluating a hypothesis with a separate test set
    • Check overfit, generalization
    • train:test = 70%:30%
  • Model selection with another separate cross validation set
    • Compare different models (# of features, degree of polynomial, and \lambda)
    • train:cv:test = 60%:20%:20%

Model selection details: J(\Theta;\lambda)

  • Number of parameters(|\Theta|) and Bias/Variance
    • bias(underfit): J_{CV}(\theta) \approx J_{train}(\theta) \gg 0, not enough parameters for task
    • variance(overfit): J_{CV}(\theta) \gg J_{train}(\theta), too many parameters for task
  • Regualization and Bias/Variance
    • Remind contribution of \lambda to J, J(\theta) = \frac{1}{2m} \sum_{i=1}^{m} (h_\theta (x^{(i)}) - y^{(i)})^2 + \frac{\lambda}{2m} \sum_{j=1}^{m} \theta_j^2
    • \lambda \approx 0 \implies \verb!maybe overfit!
    • \lambda \gg 0 \implies \verb!maybe less overfit!
  • Learning Curves, Error x training set size
    • High bias, low training size: J_{train}(\Theta) low and J_{CV}(\Theta) high
    • High bias, large training size: both J_{train}(\Theta) and J_{CV}(\Theta) high
    • High variance, low training size: J_{train}(\Theta) low and J_{CV}(\Theta) high
    • High variance, large training size: J_{train}(\Theta) OK and J_{CV}(\Theta) keep decreasing
  • Summary
    • Select best combo \Theta, \lambda, and right amount of data by checking J_{CV}
    • And also check J_{test} for good generalization

Machine Learning System Design (with Spam classifier)

  • Prioritizing What to Work On
  • Error Analysis
  • Error Metrics Skewed Classes
  • Precision and Recall trade off

Week 7

  • Support Vector Machine

Week 8

Unsupervised Learning

  • Clustering
  • K-Means Algorithm
  • Optimization Objective
  • Random Initialization
  • Choosing the Number of Clusters

Dimensionality Reduction

  • Motivation

  • Motivation I: Data Compression

  • Motivation II: Visualization

  • Principal Component Analysis

  • Principal Component Analysis Problem Formulation

  • Principal Component Analysis Problem Algorithm

  • Applying PCA

  • Reconstructin from Compressed Representation

  • Choosing the Number of Principal Components

  • Advice for Applying PCA

Week 9

Anomaly Detection

Recommender Systems

Week 10

Large Scale Machine Learning

  • Gradient Descent with Large Datasets

  • Learning with Large Datasets

  • Stochastic Gradient Descent

  • Mini-Batch Gradient Descent

  • Stochastic Gradient Descent Convergence

  • Advanced Topics

  • Online Learning

  • Map Reduce and Data Parallelism

Week 11

Application Example: Photo OCR

  • Photo OCR
  • Problem Description and Pipeline
  • Sliding Windows
  • Getting Lots of Data and Artificial Data
  • Ceiling Analysis: What Part of the Pipeline to Work on Next

Conclusion

  • Summary and Thank You