Intro and Philosophy.ipynb

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   "cells": [
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Machine learning intro"
     ]
    },
    {
     "cell_type": "heading",
     "level": 4,
     "metadata": {},
     "source": [
      "Preface - "
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "These are notes I wrote for myself while studying for PhD qualifying exams at Stanford. \n",
      "\n",
      "I wanted to published them so that others could benefit.\n",
      "\n",
      "They draw from sources:\n",
      "\n",
      "* CS229, an excellent machine learning class at Stanford taught by Andrew Ng.\n",
      "* An Introduction to Statistical Learning (ISL), an excellent text by G. James, D. Witten,  T. Hastie and R. Tibshirani (Springer, 2013).\n",
      "* A Few Useful Things to Know about Machine Learning, Pedro Domingos. Department of Computer Science and Engineering, UW.\n"
     ]
    },
    {
     "cell_type": "heading",
     "level": 4,
     "metadata": {},
     "source": [
      "Basic idea - "
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Machine learning is field of study that gives computers the ability to learn without being explicitly programmed.\n",
      "\n",
      "It does this through induction:\n",
      "\n",
      "* Deductive (top-down): A conclusion is reached reductively by applying general rules.\n",
      "* Inductive (bottom-up): A conclusion is reached by generalizing or extrapolating from initial information.\n",
      "\n",
      "Learners turn a small amount of input knowledge into a large amount of output knowledge.\n",
      "\n",
      "* Programming is a lot of work. We have to build everything from scratch. \n",
      "* Learning is more like farming, which lets nature do most of the work.\n",
      "\n",
      "The input knowledge for induction is captured in a training set of data.\n",
      "\n",
      "The goal of machine learning is to generalize beyond the examples in the training set.\n",
      "\n",
      "In order to do this, the learner must embody knowledge or assumptions beyond the data it\u2019s given in order to generalize beyond it."
     ]
    },
    {
     "cell_type": "heading",
     "level": 4,
     "metadata": {},
     "source": [
      "Types of learning - "
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "(1) Supervised learning: $learn$ a function, $h :x \\mapsto y $, so that $h(x)$ is a good predictor of $y$. \n",
      "\n",
      "* Regression\n",
      "* Classification, which outputs a single discrete value (e.g., the class).\n",
      "\n",
      "(2) Unsupervised learning: let the computer determine structure and patterns in data:\n",
      "\n",
      "* Clustering problems\n",
      "* Classifying sub-populations of single cells\n"
     ]
    },
    {
     "cell_type": "heading",
     "level": 4,
     "metadata": {},
     "source": [
      "General architecture of supervised learning algorithms - "
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Representation: Learner must be represented in a formal language that the computer can handle. For example, some classifiers.\n",
      "\n",
      "* Decision trees\n",
      "* Neural networks\n",
      "* Hyperplanes (e.g., Naive Bayes, Logistic regression)\n",
      "* Instances (e.g., SVMs)\n",
      "\n",
      "Evaluation function: We use this to distinguish good learners from bad ones.\n",
      "\n",
      "* Accuracy/Error rate\n",
      "* Precision and recall\n",
      "* Squared error\n",
      "* Likelihood\n",
      "* K-L divergence\n",
      "\n",
      "Optimization (learning strategy): The method we use to search for the highest-scoring learner.\n",
      "\n",
      "* Unconstrained continuous optimization (e.g., Gradient descent, Quasi-Newton methods)\n",
      "* Constrained continuous optimization (e.g., Quadratic programming)\n",
      "* Combinatorial optimization (e.g., Greedy search)\n",
      "\n",
      "For example, let's consider a practical case. \n",
      "\n",
      "* We choose logistic regression, a parametric hyperplane representation, to classify our data. \n",
      "* We evaluate it using a likelihood funtion.\n",
      "* We train the optimal paramters using unconstrained continuous optimization, gradient ascent."
     ]
    },
    {
     "cell_type": "heading",
     "level": 4,
     "metadata": {},
     "source": [
      "More speciifc notes on supervised learning algorithms - "
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "We start with training data, a set of values ($X^1_i$ to $X^1_n$) for each target $Y^1$.\n",
      "\n",
      "* $X$, a set of measured features.\n",
      "* $Y$, a response.\n",
      " \n",
      "There exists some $f$ that captures the relationship between $Y$ and $X$.\n",
      "\n",
      "Machine learning refers to a set of approaches for estimating $f$.\n",
      "\n",
      "$Y = f(X) + \\epsilon$\n",
      "\n",
      "$f$:\n",
      "\n",
      "* Parametric - A fixed size, like linear classifiers, and fixed form for $f$ with pre-defined parameters.\n",
      "* Non-parametric - Can grow with the data, like decision treesand can fit a wider range of possible shapes for $f$.\n",
      "\n",
      "$Y$:\n",
      "\n",
      "* Continuous: Regression methods for $f$ are used here.\n",
      "* Categorical: Classification methods for $f$ are used here.\n",
      "\n",
      "$\\epsilon$:\n",
      "\n",
      "* $Y$ is also a funtion of $\\epsilon$, which cannot be predicted with respect to $X$.\n",
      "* Irreducible error: Errors in our model due to $\\epsilon$ cannot be reduced using a different statistical learning model. \n",
      "* Reducible error: Errors in our model that can be reduced using a different statistical learning model. \n",
      "* Irreducible error will always provide an upper bound on the accuracy of our prediction for $Y$.\n"
     ]
    },
    {
     "cell_type": "heading",
     "level": 4,
     "metadata": {},
     "source": [
      "Objectives for learning - "
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Our choice for $f$ is informed by:\n",
      "\n",
      "* Inference: How does $Y$ change with respect to different values of $X$.\n",
      "* Prediction: Predict a value for $Y$ with respect to $X$."
     ]
    },
    {
     "cell_type": "heading",
     "level": 4,
     "metadata": {},
     "source": [
      "Error modes - "
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Above, we mentioned that we use an evaluation function to distinguish good learners from bad ones.\n",
      "\n",
      "For example, mean squared error: $T =$ Avg $(y_o - \\hat{f}(x_o))^2 = B^2 + V + var(\\epsilon)$\n",
      "\n",
      "Note that is is composed of:\n",
      "\n",
      "* Bias, $B$: Error introduced by using a model that does not capture the actual relationship (e.g., under-fitting). \n",
      "* Variance, $V$: Amount by which $\\hat{f}$ would change if we estimated it using a different training data set (e.g., over-fitting).\n"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import os\n",
      "from IPython.display import Image\n",
      "i = Image(filename=os.getcwd()+'/Images/Bias_Variance.jpg') # from the ISL text.\n",
      "i"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
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ScTZTi8JP+1sw4dynLKry+jhJWyfG55j82w+YYnL8Xl2IzDwcvpVcHwNwdwxm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fZZPiSusaQAfSHgHx94O+KXgzw38Q/h94h0/xV4M8XaXb6z4e1/S5He01CwuQcNsmSK5tLq3l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\n2Q==\n",
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 2,
       "text": [
        "<IPython.core.display.Image at 0x1047f6390>"
       ]
      }
     ],
     "prompt_number": 2
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "What if the knowledge and data we have are not sufficient to completely determine the correct classifier? \n",
      "\n",
      "Then we run the risk of just hallucinating a classifier: quirks in the data.\n",
      "\n",
      "Causes for this:\n",
      "\n",
      "* Noise: incorrect labeling.\n",
      "* Bias: High bias is driven by rigididy; it can only model a strict relationship in the data.\n",
      "* Variance: High variance is driven by lack of rigidity; ability to fit training data. \n",
      "\n",
      "Trade-offs between bias and variance:\n",
      "\n",
      "* A linear learner has high bias, because it only works if two classes is are seperated by a hyperplane.\n",
      "* Decision trees don\u2019t have this problem because they can represent any Boolean function.\n",
      "* But on the other hand they can suffer from high variance. \n",
      "* Decision trees learned on different training sets generated by the same phenomenon are often very different.\n"
     ]
    },
    {
     "cell_type": "heading",
     "level": 4,
     "metadata": {},
     "source": [
      "De-bugging learning algorithms - "
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "As mentioned, there are at least two obvious failure modes.\n",
      "\n",
      "Overfitting (High variance):\n",
      "\n",
      "* Fitting high order polynomial to linear data.\n",
      "* In this case, training error will be much lower than test error. \n",
      "\n",
      "Underfitting (High bias):\n",
      "\n",
      "* Fitting low order polynomial to complex data.\n",
      "* In this case, training error will also be high. \n",
      "\n",
      "We can use diagnostics to understand why the algorithm is not working.\n",
      "\n",
      "Learning curve:\n",
      "\n",
      "* Plot both test and training error with respect to the training set size.\n",
      "* The test set error will usually go down as we increace the training set size.\n",
      "* The training error usually goes up with respect to m, the training set size.\n",
      "* It becomes harder to fit the model to more training examples well.\n",
      "\n",
      "Result are indicative of the error mode:\n",
      "\n",
      "* Overfitting: A large gap between test and training is indicative of overfitting. To resolve, get more training data.\n",
      "* Underfitting: Test and training error are similarly poor. So, here want to get a different model in order to better fit our data.\n",
      "    \n",
      "In summary, these approaches should be used to address high variance (overfitting):\n",
      "\n",
      "* More training examples.\n",
      "* Feature engineering: Smaller set of features.\n",
      "\n",
      "Ways to combat overfitting:\n",
      "\n",
      "* Cross-validation.\n",
      "* Adding a regularization term to the evaluation function (e.g., to penalize classifiers with more structure).\n",
      "* Larger set of features.\n"
     ]
    },
    {
     "cell_type": "heading",
     "level": 4,
     "metadata": {},
     "source": [
      "General notes - "
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "(1) Start with simple learning systems:\n",
      "\n",
      "* It is hard to predict where a system will fail, *a priori*.\n",
      "\n",
      "(2) Avoid over-theorizing:\n",
      "\n",
      "* Ensure your field of theoretical work is relevant to the application of interest.\n",
      "\n",
      "(3) As a rule of thumb, a dumb algorithm with lots and lots of data beats a clever one with modest amounts of it."
     ]
    }
   ],
   "metadata": {}
  }
 ]
}