Updates to lab classes for MLSS

Author: lawrennd

lab_classes/mlss/.ipynb_checkpoints/index-checkpoint.ipynb

@@ -1,7 +1,7 @@
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-  "signature": "sha256:43e13b0d3446c3bca3497c75aa3b092226d220c8b4dcd5f679f4e662bfd988e9"
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@@ -51,7 +51,7 @@
       "\n",
       "## Gaussian Processes\n",
       "\n",
-      "The second day will focus on Gaussian process models and developing covariance functions. \n",
+      "The session will focus on Gaussian process models and developing covariance functions. \n",
       " \n",
       "* [Introduction to Gaussian Processes](./gaussian process introduction.ipynb) We move from the Bayesian regression with polynomials to Gaussian process perspectives by looking at the priors over the function directly.\n",
       "* [GPy: Introduction through Covariance Functions](./GPy introduction covariance functions.ipynb) `GPy` is a Python Gaussian process framework that implements many of the ideas we'll see in the course. In this session we introduce its covariance functions and sample from the associated Gaussian processes.\n",

lab_classes/mlss/GPy gaussian process regression.ipynb

@@ -1,7 +1,7 @@
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  "metadata": {
   "name": "",
-  "signature": "sha256:4a95cc8ac4784d9884e9eca006446655b954da06402f08df15653d9139d722e2"
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@@ -14,9 +14,9 @@
      "source": [
       "# Gaussian Process Regression in GPy\n",
       "\n",
-      "## Gaussian Process Winter School, Genova, Italy\n",
+      "## Machine Learning Summer School, Sydney, Australia\n",
       "\n",
-      "### 20th January 2014\n",
+      "### February 2015\n",
       "\n",
       "### Neil D. Lawrence and Nicolas Durrande\n",
       "\n",

lab_classes/mlss/GPy introduction covariance functions.ipynb

@@ -1,7 +1,7 @@
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  "metadata": {
   "name": "",
-  "signature": "sha256:81d15f4e5504b5e3a79a177c06233272245e10b15057208834e75123cc7f2775"
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@@ -13,9 +13,10 @@
      "metadata": {},
      "source": [
       "# GPy Introduction: Covariance Functions in GPy\n",
-      "## Gaussian Process Winter School, Genova, Italy\n",
       "\n",
-      "### 20th January 2014\n",
+      "## Machine Learning Summer School, Sydney, Australia\n",
+      "\n",
+      "### February 2015\n",
       "\n",
       "### Neil D. Lawrence and Nicolas Durrande\n"
      ]

lab_classes/mlss/GPy optimizing gaussian processes.ipynb

@@ -1,7 +1,7 @@
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  "metadata": {
   "name": "",
-  "signature": "sha256:e536d3195e4f11c355f3ffa9515f59741de6135d0301c7d95ce8436884cac106"
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  "nbformat_minor": 0,
@@ -14,9 +14,9 @@
      "source": [
       "# Introduction to GPy: Gaussian Process Regression in GPy\n",
       "\n",
-      "## Gaussian Process Winter School, Genova, Italy\n",
+      "## Machine Learning Summer School, Sydney, Australia\n",
       "\n",
-      "### 20th January 2014\n",
+      "### February 2015\n",
       "\n",
       "### Neil D. Lawrence and Nicolas Durrande\n"
      ]

lab_classes/mlss/gaussian process introduction.ipynb

@@ -14,8 +14,8 @@
      "source": [
       "# Inroduction to Gaussian Processes\n",
       "\n",
-      "## Gaussian Process Road Show, Genoa, Italy\n",
-      "### 19th or 20th January 2015\n",
+      "## Machine Learning Summer School, Sydney, Australia\n",
+      "### February 2015\n",
       "### Neil D. Lawrence\n",
       "\n",
       "When we form a Gaussian process we assume data is *jointly Gaussian* with a particular mean and covariance,\n",

lab_classes/mlss/index.ipynb

@@ -1,7 +1,7 @@
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  "nbformat": 3,
  "nbformat_minor": 0,
@@ -51,7 +51,7 @@
       "\n",
       "## Gaussian Processes\n",
       "\n",
-      "The second day will focus on Gaussian process models and developing covariance functions. \n",
+      "The session will focus on Gaussian process models and developing covariance functions. \n",
       " \n",
       "* [Introduction to Gaussian Processes](./gaussian process introduction.ipynb) We move from the Bayesian regression with polynomials to Gaussian process perspectives by looking at the priors over the function directly.\n",
       "* [GPy: Introduction through Covariance Functions](./GPy introduction covariance functions.ipynb) `GPy` is a Python Gaussian process framework that implements many of the ideas we'll see in the course. In this session we introduce its covariance functions and sample from the associated Gaussian processes.\n",