From f1a026847c20c6adc470d68278f2671174bfe2db Mon Sep 17 00:00:00 2001 From: Neil Lawrence Date: Sun, 1 Feb 2015 14:41:23 +0000 Subject: [PATCH] Updates to lab classes for MLSS --- lab_classes/mlss/.ipynb_checkpoints/index-checkpoint.ipynb | 4 ++-- lab_classes/mlss/GPy gaussian process regression.ipynb | 6 +++--- .../mlss/GPy introduction covariance functions.ipynb | 7 ++++--- lab_classes/mlss/GPy optimizing gaussian processes.ipynb | 6 +++--- lab_classes/mlss/gaussian process introduction.ipynb | 4 ++-- lab_classes/mlss/index.ipynb | 4 ++-- 6 files changed, 16 insertions(+), 15 deletions(-) diff --git a/lab_classes/mlss/.ipynb_checkpoints/index-checkpoint.ipynb b/lab_classes/mlss/.ipynb_checkpoints/index-checkpoint.ipynb index bdd79f7..02f6e9b 100644 --- a/lab_classes/mlss/.ipynb_checkpoints/index-checkpoint.ipynb +++ b/lab_classes/mlss/.ipynb_checkpoints/index-checkpoint.ipynb @@ -1,7 +1,7 @@ { "metadata": { "name": "", - "signature": "sha256:43e13b0d3446c3bca3497c75aa3b092226d220c8b4dcd5f679f4e662bfd988e9" + "signature": "sha256:0556b4ae1080bb2506a261f0aed7304355929147020e90795dffc24fe67d17f9" }, "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", diff --git a/lab_classes/mlss/GPy gaussian process regression.ipynb b/lab_classes/mlss/GPy gaussian process regression.ipynb index f790bc1..e6f9e99 100644 --- a/lab_classes/mlss/GPy gaussian process regression.ipynb +++ b/lab_classes/mlss/GPy gaussian process regression.ipynb @@ -1,7 +1,7 @@ { "metadata": { "name": "", - "signature": "sha256:4a95cc8ac4784d9884e9eca006446655b954da06402f08df15653d9139d722e2" + "signature": "sha256:6f75c774ee9379cd0d148d6a0826a50b406af86f66fb8c00fd8b97d54beee7fe" }, "nbformat": 3, "nbformat_minor": 0, @@ -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", diff --git a/lab_classes/mlss/GPy introduction covariance functions.ipynb b/lab_classes/mlss/GPy introduction covariance functions.ipynb index 19fecef..31e0a09 100644 --- a/lab_classes/mlss/GPy introduction covariance functions.ipynb +++ b/lab_classes/mlss/GPy introduction covariance functions.ipynb @@ -1,7 +1,7 @@ { "metadata": { "name": "", - "signature": "sha256:81d15f4e5504b5e3a79a177c06233272245e10b15057208834e75123cc7f2775" + "signature": "sha256:823d802015716b6086a983b2cd31944abe7736a085332a4b23914230516a3d01" }, "nbformat": 3, "nbformat_minor": 0, @@ -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" ] diff --git a/lab_classes/mlss/GPy optimizing gaussian processes.ipynb b/lab_classes/mlss/GPy optimizing gaussian processes.ipynb index dfeaae2..87f1285 100644 --- a/lab_classes/mlss/GPy optimizing gaussian processes.ipynb +++ b/lab_classes/mlss/GPy optimizing gaussian processes.ipynb @@ -1,7 +1,7 @@ { "metadata": { "name": "", - "signature": "sha256:e536d3195e4f11c355f3ffa9515f59741de6135d0301c7d95ce8436884cac106" + "signature": "sha256:c12ffa19d91c1d586504d92774e872d05d6e8d63d996f07aa870cf333acea7fb" }, "nbformat": 3, "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" ] diff --git a/lab_classes/mlss/gaussian process introduction.ipynb b/lab_classes/mlss/gaussian process introduction.ipynb index 4d8ffb6..ae49590 100644 --- a/lab_classes/mlss/gaussian process introduction.ipynb +++ b/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", diff --git a/lab_classes/mlss/index.ipynb b/lab_classes/mlss/index.ipynb index bdd79f7..02f6e9b 100644 --- a/lab_classes/mlss/index.ipynb +++ b/lab_classes/mlss/index.ipynb @@ -1,7 +1,7 @@ { "metadata": { "name": "", - "signature": "sha256:43e13b0d3446c3bca3497c75aa3b092226d220c8b4dcd5f679f4e662bfd988e9" + "signature": "sha256:0556b4ae1080bb2506a261f0aed7304355929147020e90795dffc24fe67d17f9" }, "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",