Data Science and Machine Intelligence use cases in Africa 2017

Dear MIIA community,

UPDATE: This post has been updated with links to the video as well as presentations of the MIIA event on 23 February 2017

Herewith the links to the video and presentations of another successful Meetup in Cape Town with about 70 people attending. As mentioned at the Meetup we have also started a MIIA chapter in Johannesburg/Pretoria:

MIIA Meetup in Cape Town:

MIIA Meetup in Johannesburg/Pretoria:

NEW: You are also welcome to join an upcoming international webinar where MIIA will be represented on March 9, 2017“How AI is Transforming Smart Manufacturing” – hosted by Avid Solutions, USA. You can register here:


In follow-up to last month’s collaboration between Machine Intelligence Institute of Africa (MIIA) and Data Science Nigeria, where a number of MIIA’s Data Science experts participated in a Q&A panel session that was part of a Data Science bootcamp (with 140 participants) in Nigeria, we are hosting our first MIIA Meetup in 2017 on Thursday 23 February at Cape Innovation and Technology Initiative (CiTi), Woodstock Exchange, Cape Town, which is sponsored by Cortex Logic and CiTi. As can be seen from the agenda below, we are showcasing an interesting range of Data Science and Machine Intelligence use cases and topics by members of the MIIA community. A number of the presentations also speaks to the theme discussed in last year’s post, Solving Intelligence, Solving Real-world problems. In further support of this theme, we are also planning a number of presentations and demos in 2017 showcasing state-of-the-art Deep Learning applications. The meetup will kick-off at 6pm with presentations and Q&A (approximately an hour and 15 minutes) followed by sufficient time for networking and discussions afterwards. See details about the event below.


MIIA Meetup 23 February 2017

Video of Meetup: 


  1. Introduction – Dr Jacques Ludik – MIIACortex LogicBennit.AI
  2. Emotion recognition: state of the art networks driving deeper immersive experiences – Dr Mike GrantFrans Cronje – DataProphet
  3. Intersections: Network Science, Behavioural Science, & Data Science – Dr Eli Grant – Behavioural Science and Impact Analysis, Oxford Refugee Studies Centre, International Astronomical Union Office of Astronomy for Development, University of Oxford, UK
  4. Distinguishing plants using metabolic barcodes and random forest – Dr Millie Hilgart – Consultant on the experimental design and statistical analysis of field trials of various products. Previously: Post doc Research Fellow, Botany / Bioinformatics / Molecular Biology, University of Cape Town
  5. Data Science at the Centre for High Performance Computing – Dr Catherine Cress – Principal Research Scientist, Center for High Performance Computing 
  6. Visualization of Twitter Data in Python – Rockefeller, Master of Mathematical Science at AIMS, Postgraduate School of Sciences, Technology and Geosciences of University of Yaoundé, Cameroon

See also the MIIA Events page for links to videos and presentations of other MIIA events.

Venue: CiTi, Bandwidth Barn, 3rd Floor, Woodstock Exchange, Cape Town, South Africa

Parking is available in the basement and parking bays at the back of Woodstock Exchange building

See more details also here:



Machine intelligence Institute of Africa (MIIA):

Some Recent MIIA posts

Joining Machine Intelligence Institute of Africa (MIIA)

Some recent Deep Learning Links

  • Nuts and Bolts of Applying Deep Learning (Andrew Ng) –
  • NIPS 2016 (Dec 2016)
  • 50 things I learned at NIPS 2016:
  • Deep Learning Patterns, Methodology and Strategy -
  • Deep Learning School (Stanford) – Sep 2016 –

1. Foundations of Deep Learning (Hugo Larochelle, Twitter) –

2. Deep Learning for Computer Vision (Andrej Karpathy, OpenAI) –

3. Deep Learning for Natural Language Processing (Richard Socher, Salesforce) –

4. TensorFlow Tutorial (Sherry Moore, Google Brain) –

5. Foundations of Unsupervised Deep Learning (Ruslan Salakhutdinov, CMU) –

6. Nuts and Bolts of Applying Deep Learning (Andrew Ng) –

7. Deep Reinforcement Learning (John Schulman, OpenAI) –

8. Theano Tutorial (Pascal Lamblin, MILA) –

9. Deep Learning for Speech Recognition (Adam Coates, Baidu) –

10. Torch Tutorial (Alex Wiltschko, Twitter) –

11. Sequence to Sequence Deep Learning (Quoc Le, Google) –

12. Foundations and Challenges of Deep Learning (Yoshua Bengio) –

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