Ernest Fokoue

Professor

School of Mathematics and Statistics

College of Science

epfeqa@rit.edu

585-475-7525

Office Hours

Tuesday, 9:30am - 10:30am Friday, 12:30pm - 1:00pm Thursday, 9:30am - 10:30am

Office Location

HLC-2517

Ernest Fokoue

Professor

School of Mathematics and Statistics

College of Science

Bio

I have been around Mathematical Sciences from my very early childhood, and I have always greatly enjoyed the beauty and power that mathematics affords to problem solving. Statistical Science in particular and specifically Statistical Machine Learning and Data Science are my passion, my profession and to some extent my vocation. The flexibility and power of probabilistic reasoning along with the richness and vast applicability of statistical models both fascinate and enthuse me immensely. My mission as a professor of statistical science is to generously, joyfully and rigorously share the full extent of my knowledge and expertise in this exciting field with the ultimate goal of empowering all the students that God graciously sends my way. Indeed, I always envision my students acquiring state-of-the-art knowledge in statistical science and ultimately developing into world class experts and pioneers.

epfeqa@rit.edu

585-475-7525

Personal Links

Curriculum Vitae

Currently Teaching

MATH-790

Research & Thesis

0 - 9 Credits

Masters-level research by the candidate on an appropriate topic as arranged between the candidate and the research advisor.

STAT-305

Regression Analysis

3 Credits

This course covers regression techniques with applications to the type of problems encountered in real-world situations. It includes use of the statistical software SAS. Topics include a review of simple linear regression, residual analysis, multiple regression, matrix approach to regression, model selection procedures, and various other models as time permits.

STAT-495

Undergraduate Research in Statistical Science

1 - 3 Credits

This course is a faculty-directed project that could be considered original in nature. The level of work is appropriate for students in their final two years of undergraduate study.

STAT-547

Data Mining

3 Credits

The use of statistical models in computer algorithms allows users to make decisions and predictions, and to perform tasks that traditionally require human cognitive abilities. Data mining and Machine learning are interdisciplinary fields at the intersection of statistics, computer science, applied mathematics which develops such statistical models and interweaves them with computer algorithms. It underpins many modern technologies, such as speech recognition, Internet search, bioinformatics and computer vision. The course will provide an introduction to Statistical Machine Learning and its core models and algorithms.

STAT-747

Principles of Statistical Data Mining

3 Credits

This course covers topics such as clustering, classification and regression trees, multiple linear regression under various conditions, logistic regression, PCA and kernel PCA, model-based clustering via mixture of gaussians, spectral clustering, text mining, neural networks, support vector machines, multidimensional scaling, variable selection, model selection, k-means clustering, k-nearest neighbors classifiers, statistical tools for modern machine learning and data mining, naïve Bayes classifiers, variance reduction methods (bagging) and ensemble methods for predictive optimality.