Course Detail
Course Components:
This course will cover the applied fundamentals of machine learning through hands-on exercises along with lectures on some of the theoretical underpinnings differentiating learning approaches. Topics in feature selection, supervised learning, unsupervised learning, online learning, and reduction methods will be covered along with best practice predictor evaluation methods. The course will address the unique issues faced when applying machine learning to clinical and biomedical problems. Students will be expected to program real-world solutions using python and popular machine learning software packages like Weka.