Machine Learning
Description: This full-day course provides a broad introduction to machine learning and statistical pattern recognition. It covers a range of topics from basic concepts, learning theory to various supervised machine learning techniques with focus on deep learning. Several case studies will be presented where audience will learn practical skills to tackle real-world problems. We will discuss topics such as data preprocessing, model selection, training/evaluation, overfitting, etc in these case studies. Some machine learning packages such as tensorflow, Scikit-learn, and XGBoost will be used for this course.
Instructor: Weiguang Guan, SHARCNET, McMaster University.
Prerequisites: Basic Python programming skill is required. Experience with some of the machine learning packages are preferred.
Course materials: Data and code for case studies will be provided in class.
