Support vector machines and random forest
Day 3, 8:30 - 10:00 am (lecture)
Tiwonge Msulira Banda
This Lecture will continue our journey with Supervised Machine Learning, with a focus on classification problems. Two algorithms will be covered, namely Support Vector Machines (SVM) and Random Forest (RF). The two algorithms will be used to classify phishing emails from a real dataset. We will go through each algorithm step by step and compare results from the two.
Tiwonge Msulira Banda has over 14 years’ experience in ICT4D project management. He is responsible for projects and administration at UbuntuNet Alliance, the regional research and education network for Eastern and Southern Africa. He is an experienced machine learning developer, data analyst and business intelligence analyst. He has a particular interest in the development and application of data-driven systems that address real life day to day challenges. In his spare time, he likes building data visualization dashboards for whatever data he comes across. He also likes building, training and evaluating machine learning algorithms. He holds an MSc in Information Technology with Business Intelligence; a Masters in Business Administration and a BSc in Environment Science.