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-# mlpractical
-## Machine Learning Practical (INFR11119)
+# Machine Learning Practical
-**Note:** At this point, you can go straight to 00_Introduction notebook - which contains more information.
+This repository contains the code for the University of Edinburgh [School of Informatics](http://www.inf.ed.ac.uk) course [Machine Learning practical](http://www.inf.ed.ac.uk/teaching/courses/mlp/).
-To run the notebooks (and later the code you are going to write within this course)
-you are expected to have installed the following packages:
-
-
-- python 2.7+
-- numpy (anything above 1.6, 1.9+ recommended, optimally compiled with some BLAS library [MKL, OpenBLAS, ATLAS, etc.)
-- scipy (optional, but may be useful to do some tests)
-- matplotlib (for plotting)
-- ipython (v3.0+, 4.0 recommended)
-- notebook (notebooks are in version 4.0)
-
-
-You can install them straight away on your personal computer,
-there is also a notebook tutorial (00_Introduction) on how to
-do this (particularly) on DICE, and what configuration you
-are expected to have installed. For now, it suffices if you
-get the software working on your personal computers so you can
- start ipython notebook server and open the inital introductory
-tutorial (which will be made publicitly available next Monday).
-
-### Installing the software on personal computers
-
-#### On Windows:
-
-Download and install the Anaconda package
-(https://store.continuum.io/cshop/anaconda/)
-
-#### On Mac (use macports):
-
-
-- Install macports following instructions at https://www.macports.org/install.php
-- Install the relevant python packages in macports
-
-- sudo port install py27-scipy +openblas
-- sudo port install py27-ipython +notebook
-- sudo port install py27-notebook
-- sudo port install py27-matplotlib
-- sudo port select --set python python27
-- sudo port select --set ipython2 py27-ipython
-- sudo port select --set ipython py27-ipython
-
-
-
-Also, make sure that your $PATH has /opt/local/bin before /usr/bin
-so you pick up the version of python you just installed
-
-#### On DICE (we will do this during the first lab)
-
-### Getting the mlpractical repository
-
-Assuming ~/mlpractical is a target workspace you want to use during
-this course (where ~ denotes your home path, i.e. /home/user1).
-To start, open the terminal and clone the github mlpractical
-repository to your local disk:
-
-git clone https://github.com/CSTR-Edinburgh/mlpractical.git
-
-(Note: you can do it from your git account if you have one as the
-above just clone the repo as anonymous user, though it does not
-matter at this point, as you are not required to submit pull requests, but you are **welcomed** to do so if you think some aspects of the notebooks can be improved!)
-
-Naviagate to the checked out directory (cd ~/mlpractical) and type:
-
-ipython notebook
-
-This should start ipython notebook server and open the browser with the page
-listing files/subdirs in the current directory.
-
-To update the repository (for example, on Monday),
-enter ~/mlpractical and type git pull.
+This assignment-based course is focused on the implementation and evaluation of machine learning systems. Students who do this course will have experience in the design, implementation, training, and evaluation of machine learning systems.
+The code in this repository is split into:
+ * a Python package `mlp`, a [NumPy](http://www.numpy.org/) based neural network package designed specifically for the course that students will implement parts of and extend during the course labs and assignments,
+ * a series of [Jupyter](http://jupyter.org/) notebooks in the `notebooks` directory containing explanatory material and coding exercises to complement the course lectures as well as the course assignments.
+## Getting set up
+Detailed instructions for setting up a development environment for the course are given in [this file](environment-set-up.md). Students doing the course will spend part of the first lab getting their own environment set up.