teaching.bb-ai.net
Learning Materials
The main learning materials for the module have been created using
MkDocs and are hosted on Minerva. They can be reached using the
link above (or
this link).
Please Note:
You will need to be logged in on Minerva to access this
material.
I have found that you need to log into Minerva before
clicking the link, otherwise the login does not work. But you do
not need to visit the particular Minerva page of this module.
The material is in 6 Units. This may not be obvious on a small display
because the navigation bar gets hidden. But you can select them from
the menu button at the top left of the main page. If you can display
the web pages in a wide form, the Unit navigation bar becomes visible.
You are advised to also look at the other pages linked above, which
give information on the following:
- Preparation for the module, including installing Anaconda Python
3.
- Assessed work (to be finalised at the beginning of next week).
- Information about the Zoom sessions, and some links to
recordings of previous sessions.
Progressing through the module material
Your progress through the material is self paced. That is you
can go through the parts of it at different speeds depending on your
previous experience, your particular interests and the time available
to you. But you should note the following:
- The essential material of the module is in units 1-4. Unit 5
gives an overview of approaches to AI and describes several key programming tequniques
used in AI. Unit 6 contains speculation on
future developments in programming and some tips on further developing
your programming skills. Units 5 and 6 are not directly assessed but
many of you may find them interesting or useful.
Materials from Previous Versions of the Module
Previous teaching material is included below and may be of interest to
some of you. I think nearly all that you can find below is also
covered by the new material; however, most of the videos are different
and include some different examples and explanations.
-
Part 1: Introduction
Lesson materials
Exercises
To do the exercises you need to download the Jupyter notebook
file (with extension .ipynb)
and load it into Jupter. Full instructions and additional
information are included with the notebook files.
A 'View' link (to a HTML page) is also provided, if you just want to
have a look at the file without opening it in Jupyter.
(But do not try using the HTML file to do the exercise!)
- Create a Personal Log using Jupyter Markdown:
- Use this template to start creating your
Personal Log notebook file
[View]
[Download]
The template suggests a variety of tasks for you to try,
to help you learn about the features of Markdown.
- Beginners Python exercises:
-
Part 2: Programs, Information and Data
-
Part 3: Data Transformations and Analysis
- This notebook presents an example of how one can
analyse text in terms of letter frequency.
-
Part 4: Data Acquisition and Visualisation
- Introduction to Maps and Geographic Data using
ipyleaflet.
A PDF is provided, but it is recommended you
download and experiment with the
.ipynb notebook file:
-
Part 5: Programming for Artificial Intelligence
Data Examples
- Example data files.
This page has links to various data files that are used
as examples in the module exercises and assignments.
Data Resources
- Data Resource Links. This page contains a variety of
links to different data resources, which could be useful for your
data analysis project.
Introductory Python and Jupyter tutorials
-
Python Tutorial
--- Python information and tutorial with interactive code examples, provided
by W3schools.
-
Jupyter
Tutorial --- This is a nice tutorial by DataQuest on using
Jupyter notebook.
Slides and Videos from the 2019 Module Materials
Materials from last year are being updated and improved.
However, you may still find this material useful.
Other Resources
- A further large set of Python materials and videos are available from
the Programming for the Web
website, which was my previous Python programming module. That module
is a bit different because it does not use Jupyter (the demo videos
use the Spyder environment for Python coding, which also comes with
Anaconda).