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    "# Intro\n",
    "\n",
    "Andreas Mueller\n",
    "NYU Center for Data Science\n",
    "\n",
    "## Clone this repository:\n",
    "https://github.com/amueller/advanced_training\n",
    "\n",
    "## Schedule\n",
    "\n",
    "Start: 9am\n",
    "End: 5pm\n",
    "\n",
    "Lunch: ~11:45am\n",
    "Snack: ~3pm"
   ]
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    "# Outline\n",
    "\n",
    "## 1 Basic algorithms\n",
    "- Review of supervised learning\n",
    "- Linear models for classification and regression\n",
    "- Loss functions, regularization, empirical risk minimization\n",
    "- Path algorithms\n",
    "\n",
    "## 2 Preprocessing\n",
    "- Scaling and normalization\n",
    "- Continuous and discrete features\n",
    "- Feature selection\n",
    "    - Univariate\n",
    "    - Model-based\n",
    "    - RFE\n",
    "    - Forward / backward selection\n",
    "- Polynomial and interaction features\n",
    "\n",
    "\n",
    "## 3 Basic tools\n",
    "- Cross-validation vs train/test split\n",
    "- GridSearchCV\n",
    "- Overfitting Parameters\n",
    "- Scoring Metrics\n",
    "\n",
    "## 4 Advanced Supervised Learning\n",
    "- Decision Tree Recap\n",
    "- Random Forests\n",
    "- Gradient Boosting / xgboost\n",
    "- Kernel SVMs\n",
    "- Kernel approximation\n",
    "- Neural Networks\n",
    "\n",
    "## 5 Advanced tools\n",
    "- Pipelines\n",
    "- FeatureUnion\n",
    "- Function Transformer\n",
    "\n",
    "\n",
    "## 6 Unsupervised feature extraction and visualization\n",
    "- PCA\n",
    "- NMF\n",
    "- Robust PCA\n",
    "- TSNE\n",
    "\n",
    "\n",
    "## 7 Outlier Detection\n",
    "- Elliptic Envelope\n",
    "- IForest\n",
    "- KDE\n",
    "- robust PCA\n",
    "\n",
    "## 8 Gaussian Processes\n",
    "\n",
    "\n",
    "## 9 beyond standard sklearn\n",
    "- out of core\n",
    "- custom estimators\n",
    "\n"
   ]
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