My first kaggle submission

 

http://www.kaggle.com/c/titanic-gettingStarted/leaderboard

Details + Code coming soon…

Machine Learning Course Summary (Part 6)

Summary from the Stanford's Machine learning class by Andrew Ng

---

• Part 1
  • Supervised vs. Unsupervised learning, Linear Regression, Logistic Regression, Gradient Descent
• Part 2
  • Regularization, Neural Networks
• Part 3
  • Debugging and Diagnostic, Machine Learning System Design
• Part 4
  • Support Vector Machine, Kernels
• Part 5
  • K-means algorithm, Principal Component Analysis (PCA) algorithm
• Part 6
  • Anomaly detection, Multivariate Gaussian distribution
• Part 7
  • Recommender Systems, Collaborative filtering algorithm, Mean normalization
• Part 8
  • Stochastic gradient descent, Mini batch gradient descent, Map-reduce and data parallelism

---

Anomaly detection

• Examples
  • Fraud Detection
    • x(i) = features of users i activities.
    • Model p(x) from data
    • Identify unusual  users by checking  which have p(x) < epsilon
  • Manufacturing
  • Monitoring computers in a data center
• Algorithm
  • Choose features x(i) that you think might be indicative of anomalous examples.
  • Fit parameters u1,…un, sigma square 1,… sigma square n
  • Given new example x, compute p(x):
  • Anomaly if p(x) < epsilon

• Aircraft engines example
  • 10000     good (normal) engines
  • 20     flawed engines (anomalous)
  • Alternative 1
    • Training set: 6000 good engines
    • CV: 2000 good engines (y=0), 10 anomalous (y=1)
    • Test: 2000 good engines (y=0), 10 anomalous (y=1)
  • Alternative 2:
    • Training set: 6000 good engines
    • CV: 4000 good engines (y=0), 10 anomalous (y=1)
    • Test: 4000 good engines (y=0), 10 anomalous (y=1)
  • Algorithm Evaluation

• Anomaly detection vs. Supervised learning

  Anomaly detection	Supervised learning
  Very small number of positive examples	Large number of positive and negative examples.
  Large number of negative examples	Enough positive examples for algorithm to get a sense of what positive examples are like, future positive examples likely to be similar to ones in training set.
  Many different “types” of anomalies. Hard for any algorithm to learn from positive examples what the anomalies look like; future anomalies may look nothing like any of the anomalous examples we’ve seen so far.
  Fraud detection	Email spam classification
  Manufacturing (e.g. aircraft engines)	Weather prediction (sunny/rainy/etc).
  Monitoring machines in a data center	Cancer classification
• Choose what features to use
  • Plot a histogram and see the data

• Original vs. Multivariate Gaussian model

Machine Learning Course Summary (Part 5)

Summary from the Stanford's Machine learning class by Andrew Ng

---

• Part 1
  • Supervised vs. Unsupervised learning, Linear Regression, Logistic Regression, Gradient Descent
• Part 2
  • Regularization, Neural Networks
• Part 3
  • Debugging and Diagnostic, Machine Learning System Design
• Part 4
  • Support Vector Machine, Kernels
• Part 5
  • K-means algorithm, Principal Component Analysis (PCA) algorithm
• Part 6
  • Anomaly detection, Multivariate Gaussian distribution
• Part 7
  • Recommender Systems, Collaborative filtering algorithm, Mean normalization
• Part 8
  • Stochastic gradient descent, Mini batch gradient descent, Map-reduce and data parallelism

---

K-means Algorithm

• Clustering

• K-means algorithm
  • Put random cluster centroids
  • Find mean and replot cluster centroids
  • repeat…

• *Note – If a centroid has NO POINTS assigned to it, than eliminate that cluster centroid

• If we randomly choose WRONG X values than it might get stuck in “local optima”

• To solve the above random initialization problem, we run it 100 times and pick the clustering that gave lowest cost.
  • Note:
    • if K is small (between 2 to 10) than multiple random initialization will find better local optima
    • if K is large than multiple random initialization may not help or make a huge difference.

• How to choose the Number of Clusters ??
  • Elbow method

Dimensionality Reduction

• Reduce data from 2D to 1D and 3D to 2D

Principal Component Analysis (PCA) Algorithm

• Data Preprocessing

• Algorithm
  • Reduce data from n dimensions to k dimensions
  • Compute “covariance matrix”
  • Compute “eigenvectors” of matrix sigma

• Summary

• Choosing K (number of principal components)
  • Typically choose k to be smallest value:
  • 95% to 99% is a common variance value

• Keep changing K and see what gives us the smallest value which gives us 99% variance.

Applying Principal Component Analysis (PCA)

• Supervised Learning Speedup
  • Extract inputs
  • Apply PCA
  • Get New Training Set
  • Use logistic regression or other algorithms

• Application of PCA
  • Compressions
    • Reduce memory/disk needed to store data
    • Speed up learning algorithm
  • Visualization
  • DO NOT USE PCA to prevent overfitting (to reduce the number of features), use regularization instead.
  • Before implementing PCA, first try running whatever you want to do with the original/raw data. Only if that doesn’t do what you want, then implement PCA

Machine Learning Course Summary (Part 4)

Summary from the Stanford's Machine learning class by Andrew Ng

---

• Part 1
  • Supervised vs. Unsupervised learning, Linear Regression, Logistic Regression, Gradient Descent
• Part 2
  • Regularization, Neural Networks
• Part 3
  • Debugging and Diagnostic, Machine Learning System Design
• Part 4
  • Support Vector Machine, Kernels
• Part 5
  • K-means algorithm, Principal Component Analysis (PCA) algorithm
• Part 6
  • Anomaly detection, Multivariate Gaussian distribution
• Part 7
  • Recommender Systems, Collaborative filtering algorithm, Mean normalization
• Part 8
  • Stochastic gradient descent, Mini batch gradient descent, Map-reduce and data parallelism

---

Support Vector Machines

• Hypothesis and Decision Boundary 
  • Hypothesis 

• Decision Boundary

• Kernels
  • For Non Linear Decision Boundary we can use higher order polynomials but is there a different/better choice of the features ?
  • High order polynomials can be computationally heavy for image processing type problem.
  • Given X, compute new feature depending  on proximity to landmarks.

• When x = 3/5 the bump increases as its near to the landmark l(1), which is equal to [3,5], sigma square value makes the bump narrow or wide.

• Predict “1” when theta0 + theta1 f1 + theta2 f2 + theta3 f3  >= 0
• Let’s assume theta0 = -0.5, theta1 = 1, theta2 = 1, theta3 = 0
• For x(1),
  • f1 == 1 ( as its close to l(1))
  • f2 == 0 ( as its close to l(2))
  • f3 == 0 ( as its close to l(3))
• Hypothesis :
  • = theta0 + theta1 * 1 + theta2 * 0 + theta3 * 0
  • = -0.5 + 1
  • = 0.5  (which is > 0, so we predict 1 )

• How do we choose Landmarks ??
• What other similarity functions we can use other than ‘Gaussian Kernel” ??
  • * Make l same as X

• Kernels do not go well with logistic regression due to computational complexity
• SVM with Kernels are optimized for computation and runs much faster

Using SVM

Use software packages (liblinear, libsvm, …) to solve for parameters theta.

Need to Specify:

• Choice of parameter C
• Choice of Kernel (similarity function)
  1. No Kernel (“linear kernel”)
     • Predict “y=1” if theta transpose x > 0
     • No landmarks or features f(i) from x.
     • Choose this when n is large and m is small. (Number of features is large and training examples are less)
  2. Gaussian Kernel
     • Predict “y=1” if theta transpose f > 0
     • Use landmarks
     • Need to choose sigma square
     • Choose this when n is small and m is large. ( very large training set but small number of features)
     • Do perform feature scaling before using the Gaussian kernel
  3. Other choices
     • All similarity function need to satisfy a technical condition called “Mercers Theorem”
     • Polynomial kernel
     • More esoteric:
       1. string kernel
       2. chi-square kernel
       3. historgram intersection kernel

Many SVM packages already have built-in multi class classification functionality, if not use the one-vs-all method

• Logistic Regression vs. SVM

 

Machine Learning Course Summary (Part 2)

 

Summary from the Stanford's Machine learning class by Andrew Ng

---

• Part 1
  • Supervised vs. Unsupervised learning, Linear Regression, Logistic Regression, Gradient Descent
• Part 2
  • Regularization, Neural Networks
• Part 3
  • Debugging and Diagnostic, Machine Learning System Design
• Part 4
  • Support Vector Machine, Kernels
• Part 5
  • K-means algorithm, Principal Component Analysis (PCA) algorithm
• Part 6
  • Anomaly detection, Multivariate Gaussian distribution
• Part 7
  • Recommender Systems, Collaborative filtering algorithm, Mean normalization
• Part 8
  • Stochastic gradient descent, Mini batch gradient descent, Map-reduce and data parallelism

---

Regularization

• Problem of Overfitting

• Options to address overfitting:
  • Reduce number of features
    • Manually select which features to keep
    • Model selection algorithm (later in course)
  • Regularization
    • Keep all features but reduce magnitude/values of parameters theta.
    • works well when we have lots of features, each of which contributes a bit to predicting y.
• Too much regularization can “underfit” the training set and this can lead to worse performance even for examples not in the training set.

• Cost Function
  • if lamda is too large (e.g. 10¹⁰) than the algorithm results in “underfitting”(fails to fit even training set)

• Regularization with Liner Regression

• Normal Equation

• Regularization with Logistic Regression

Neural Networks

• Introduction

  • Algorithms that try to mimic the brain. Was very widely used in 80s and early 90s; popularity diminished in late 90s.

  • Send a signal to any brain sensor and it will learn to deal with it. E.g. Auditory cortex learns to see, Somatosensory cortex learn to see.
    Seeing with your tongue, human echolocation, third eye for frog.
    

• Model Representation

• Forward propagation

• Non-linear classification example: XOR/XNOR

• Non-linear classification example: AND

• Non-linear classification example: OR

• XNOR

• Multi-class classification

• Cost Function

• Unlike logistic regression we DO NOT sum the value of “Bias Unit” in the regularization term for cost of neural networks.
• Just as logistic regression a large value of “lamda” will penalize large parameter values, thereby, reducing the changes of overfitting the training set.

• Backpropagation algorithm

• Unrolling parameters

• Gradient Checking
  • There may be bugs in forward/back propagation algorithms even if the cost function looks correct.
  • Gradient checking helps identify these bugs.

• Implementation Note:
  • Implement backprop to compute DVec (unrolled ).
  • Implement numerical gradient check to compute gradApprox.
  • Make sure they give similar values.
  • Turn off gradient checking. Using backprop code for learning
• Be sure to disable your gradient checking code before training your classifier. If you run numerical gradient computation on
  every iteration of gradient descent (or in the inner loop of costFunction(…))your code will be very slow.

• Random initialization
  • Initializing theta to 0 works for logistic regression but it does not work for neural network.
  • If we initialize theta to 0 than for neural network, after each update, parameters corresponding to inputs going to each of two hidden units are identical.
  • This causes the “Problem of Symmetric Weight”
  • To solve this issue randomly initialize the theta values.

• Training a neural network