notebooks/Chapter_04-Linear_Models_for_Classification.ipynb

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    "# Chapter 4 - Linear Models for Classification\n",
    "\n",
    "## 4.1 - Discriminant Functions\n",
    "\n",
    "### 4.1.1 - Two classes\n",
    "### 4.1.2 - Multiple classes\n",
    "### 4.1.3 - Least squares for classification\n",
    "### 4.1.4 - Fisher's linear discriminant\n",
    "### 4.1.5 - Relation to least squares\n",
    "### 4.1.6 - Fisher's dicriminant for multiple classes\n",
    "### 4.1.7 - The perceptron algorithm\n",
    "\n",
    "## 4.2 - Probabilistic Generative Models\n",
    "\n",
    "### 4.2.1 - Continuous inputs\n",
    "### 4.2.2 - Maximum likelihood solution\n",
    "### 4.2.3 - Discrete features\n",
    "### 4.2.4 - Exponential family\n",
    "\n",
    "## 4.3 - Probabilistic Discriminative Models\n",
    "\n",
    "### 4.3.1 - Fixed basis functions\n",
    "### 4.3.2 - Logistic regression\n",
    "### 4.3.3 - Iterative reweighted least squares\n",
    "### 4.3.4 - Multiclass logistic regression\n",
    "### 4.3.5 - Probit regression\n",
    "### 4.3.6 - Canonical link functions\n",
    "\n",
    "## 4.4 - The Laplace Approximation\n",
    "\n",
    "### 4.4.1 - Model comparison and BIC\n",
    "\n",
    "## 4.5 - Bayesian Logistic Regression\n",
    "\n",
    "### 4.5.1 - Laplace approximation\n",
    "### 4.5.2 - Predictive distribution"
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