Teaching

Capstone Research Project Course

Semester: 

Fall

Offered: 

2019
Students in the Capstone course apply skills such as machine learning, statistics, data management, and visualization to solve real-world problems. In groups of three to four, students identify a complex and open-ended problem and work with the instructor, mentors, and industry partners to propose a solution in the form of a software package, a set of recommendations in a report, or a research paper.  Upon completion of this challenging project, students will be better equipped to conduct research and enter the professional world.

Introduction to Machine Learning at University of Rwanda

Semester: 

Summer

Offered: 

2019

This course is part of the Master in Data Science offered by the African Center of Excelence in Data Science at University of Rwanda. Our course provides an introduction to machine learning and probabilistic modeling. We cover two major areas in machine learning: supervised learning, and unsupervised learning. The learning approach is a mixture of theory and practice. We...

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Data Science 2: Advanced Topics in Data Science

Semester: 

Spring

Offered: 

2019
Data Science 2 is the second half of a one-year introduction to data science. Building upon the material in Data Science 1, the course introduces advanced methods for data wrangling, data visualization, and deep neural networks, statistical modeling, and prediction. Topics include big data and database management,  multiple deep learning subjects such as CNNs, RNNs, autoencoders, and generative models as well as basic Bayesian methods, nonlinear statistical models and unsupervised learning. Read more about Data Science 2: Advanced Topics in Data Science

Data Science 2: Advanced Topics in Data Science

Semester: 

Spring

Offered: 

2018
Data Science 2 is the second half of a one-year introduction to data science. Building upon the material in Data Science 1, the course introduces advanced methods for data wrangling, data visualization, and statistical modeling and prediction. Topics include big data and database management, basic Bayesian methods, nonlinear statistical models, unsupervised learning, and topic models. The final module will consist of multiple deep learning subjects such as CNNs, RNNs and Autoencoders. The major programming languages used will be R and Python. Read more about Data Science 2: Advanced Topics in Data Science