BINUS University together with Clemson University and Biorealm proudly present a short course titled: “Artificial Intelligence: The Methods behind the Machine – Part 1.” This course is designed to bring the basics of data science to new users, with very little prerequisite knowledge being necessary. This course is meant to be a primer in a series that will provide successful participants with a wide cadre of knowledge spanning from basic data science methods to advanced implementation of statistical learning techniques (e.g., artificial intelligence, deep learning, etc.). This course will be guided by Dr. Christopher S. McMahan from Clemson University.
The thrust of the proposed course is to expose the end user to many traditional statistical methods which are essential in the modern data science realm. The courses will consist of a brief overview of prerequisite knowledge, in depth development of the various statistical methods, guided examples of their implementation, and model validation strategies. All material will be motivated by and illustrated with real world examples. In addition to lecture periods, the course also will be accompanied by lab sections were participants will be shown how to use modern statistical software to carry out the various methods covered in the class. The course will run over the course of a 3 week period, from May 8th, 2018 to May 25th, 2018 with details attached below. Each lecture session run on 19.00 – 20.30. The lab session will run for 2 hours.
- Mathematical Background: Basics of linear algebra, Optimization, and their implementation inside a computer environment.
- Probability: Common distributions, properties of the normal random variable, multivariate normal, expectation/variance of linear combinations of random variables, and theoretical results such as the central limit theorem and the delta method, etc.
- Point estimation: Method of moments estimation, maximum-likelihood estimation, quantifying uncertainty of estimators, asymptotic properties, etc.
- Hypothesis testing: General approach to hypothesis testing with background, cover standard 1 and two sample hypothesis testing procedures.
- Simple linear regression: hypothesis testing and diagnostics.
- Multiple linear regression: hypothesis testing, quantitative versus qualitative covariates, response surfaces, diagnostics, and model selection (AIC, BIC, CV, etc.).
- Binary regression: hypothesis testing, diagnostics, and model selection (AIC, BIC, CV, etc.).
The course will be held at Anggrek Campus, Bina Nusantara University for onsite course. You can also join this course by online. You can register for the course here. For more information, please contact Hery H. Mulyo: Heryhm@binus.edu.
About Dr. Christopher S. McMahan:
Currently, Dr. McMahan is an Associate Professor in the Department of Mathematical Sciences at Clemson University. He completed his Bachelor’s degree majoring in mathematics, minoring in physics, at Austin Peay State University, Clarksville, TN. He then earned a master’s degree in mathematics at Western Kentucky University, Bowling Green, Kentucky. He then completed a doctoral degree in statistics at the University of South Carolina, Columbia, SC. After completing his Ph.D., he joined Clemson University as an assistant professor, and has since been promoted to the rank of associate. In addition to his academic appointment, he also consults with Biorealm and serves as a Visiting Professor at BINUS University. Dr. McMahan’s research interests include, but are not limited to, categorical data analysis, group testing, survival data analysis, nonparametric methods, measurement error models, spatio-temporal modeling, statistical computing, Bayesian parametric/nonparametric estimation, high dimensional regression techniques, epidemiology/public health, and biomedical applications.
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