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OAU, Unilag students win BlueChip Inter-Campus Machine Learning competition

After an intensive 5-day learning bootcamp, Olajide Abdulrazzaq Folarin (from Obafemi Awolowo University, Ile-Ife, Osun State), Sadiq Aderinto and Stanley Obumneme Dukor (both of the University of Lagos, Akoka, Lagos) jointly won the first Inter-Campus Machine Learning competition.
Sponsored by Bluechip Technologies and organised by Data Science Nigeria, the three first-place winners individually earned the highest score for machine learning algorithm development. The bootcamp was facilitated by 11 visiting international experts in artificial intelligence, 5 online tutors and 15 Nigerian-based business leaders.
More than 10,000 students from 95 campuses participated in the 4-stage competition. It included a pre-qualification pre-study, a quiz, a Kaggle competition and a validation call. The top 85 students competed at the 5-day all-expenses-paid bootcamp at the Peninsula Resort, Lekki-Ajah Expressway, Lagos, from 10-14 October 2018.
At the bootcamp, the students were immersed in intensive sessions of theoretical learning, use case applications, face-to-face teaching, virtual online classes, and a hands-on hackathon using the newly launched Zindi platform for real-time model evaluation. Distinguished data scientists from leading global institutions including Google’s AI Lab, GitHub, MIIA and Bankable Frontiers Associate, USA facilitated the sessions. Leading executives from the Nigerian banking, telecommunications, energy, and investment sectors shared their invaluable contextual insights with the students.
The Inter-Campus Machine Learning competition strives to increase Nigeria’s experience and expertise in data science and to enhance the opportunities and employability of Nigerian students through an incentivized exposure to advanced world-class knowledge. Data Science is now the world’s no. 1 career, and is critical to high-impact transformation and innovation in Nigeria and worldwide.
In order to ensure the maximum inclusive participation, all the participants who travelled to the bootcamp from outside Lagos received full a travel grant, while 10 additional special provisions were made for ladies who did not meet the cut-off selection mark. The bootcamp included participants from all six geopolitical zones of Nigeria. In his welcome speech, the convener, Data Science Nigeria, Mr Bayo Adekanmbi said, “in our quest to play big in the Artificial Intelligence space, we must raise our game and combine the best of theoretical knowledge and real use cases with solution orientation, hence our tutorial approach of having world-class experts from leading AI centres in the world and local business leaders at the bootcamp”.
Olumide Soyombo, the co-founder of Bluechip Technologies Limited, shared Bluechip’s 10-year story and inspired the participants about the possibilities of artificial intelligence and how the students could leverage their knowledge to become technology entrepreneurs. Mr. Soyombo emphasized the importance of collaborative learning, network building and high-quality solution orientation. He commented, “We are proud to sponsor the 1st ever Intercampus Machine Learning Competition as part of our 10th anniversary, which is a demonstration of our commitment to knowledge development, especially in strategic areas like AI which will shape the future and make the world a better place”.
Kazeem Tewogbade, the managing director of Bluechip Technologies Limited, said “Bluechip Technologies is excited to have created a platform that brought together some of the world’s best and Nigeria’s budding talent across over 90 higher institutions. We are convinced that Nigeria is raising a new breed of experts that will play big on the global space”.
Each of the three winners received $1,000 US dollars and an opportunity for a short-term internship at the Bluechip technology firm. In addition, campus volunteers from the universities with the highest number of participants in the pre-qualification process were also rewarded for promoting and mobilizing participation in the competition. The University of Lagos, Ladoke Akintola University Ogbomoso, and the University of Ibadan campus volunteers received N150,000, N100,000 and N50,000 respectively.
Some of the speakers at the bootcamp included Matt Grasser, Director, Inclusive Fintech, Bankable Frontiers Associate, USA; Moustapha Cisse PhD, Google AI Lab; Professor Tom Dietterich, Distinguished Professor and Director, Oregon State University and the founder of BigML; Dr Emmanuel Doro, Principal Data Scientist, Jet.com, USA; Dr Sulaimon Afolabi, Argility, South Africa; Ekow Duker, ex-Chief Analytics Officer of Barclays Africa/MD IxioAnalytics, South Africa; Dr Jacques Ludik, CEO of Cortex Logic, South Africa; Karim Beguir, the founder/CEO of Instadeep AI, London; Omoju Miller PhD, Senior Data Scientist, GitHub, USA; Abiodun Modupe, PhD Researcher, University of the Witwatersrand, South Africa; Adewale Akinfaderin, Graduate Researcher, FSU & Senior Data Scientist, Lowe’s Inc., USA; Osayi Igharo, Founding and Managing partner, Ripple VC, San Francisco, SA; Robert John, Chief Data Scientist at Enter5ive and many others.
In addition to theoretical and hands-on exploration of topics like Anomaly detection, unsupervised classification, sequential rule mining, Deep Learning theories, Deep learning in Natural language processing; the bootcamp attendees spent time brainstorming on real business problems which were facilitated by the leadership teams of companies like FCMB (credit risk scoring), Terragon Group (AdTech recommendation), Guardian News (recommendation system), Octave Analytics (Financial inclusion geomapping), Axa Mansard (AI for insurance) and Microsoft (possibilities in MS Azure).
The bootcamp focussed on applying Artificial Intelligence to a real-world problem of financial inclusion in Nigeria The bootcamp’s Machine Learning Hackathon was based on loan default models for an anonymised real-world dataset of low-income customers and required the participants to use geographical data (longitude and latitude), historical usage and mobile phone behaviour to determine the likely risk of loan defaults, as captured here.

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