This is an introductory-level course in supervised learning, with a focus on regression and classification methods. The syllabus includes: linear and polynomial regression, logistic regression and linear discriminant analysis; cross-validation and the bootstrap, model selection and regularization methods (ridge and lasso); nonlinear models, splines and generalized additive models; tree-based methods, random forests and boosting; support-vector machines. Some unsupervised learning methods are discussed: principal components and clustering (k-means and hierarchical).
Type of Material:
Online Course Module
Recommended Uses:
in-class, homework, self-paced learners. This is an archived course, so it can no longer be taken for a certificate.
Technical Requirements:
A typical Web browser
Identify Major Learning Goals:
Upon the completion of the module, students will be able to:
1. describe various statistical learning principles;
2. develop different statistical learning models in R;
3. apply the machine learning methods to daily applications.
Target Student Population:
College to postgraduate students.
Prerequisite Knowledge or Skills:
Basic algebra, basic programming
Content Quality
Rating:
Strengths:
Designed by reputed professors. The materials are comprehensive and accurate. The information is sufficient. The concepts are clearly illustrated.
Potential Effectiveness as a Teaching Tool
Rating:
Strengths:
Accompanied by a free book. The learning objectives are clear. It can effectively reinforce the concepts via the materials and the quiz. The quiz at the end of each Chapter can assess students' learning outcomes.
Ease of Use for Both Students and Faculty
Rating:
Strengths:
The wiki and forums can engage students in the course.
The materials are effective to convey the purpose of the module.
It is visually appealing with the script of the video.
Concerns:
One has to login with the Stanford website to use the material.
Other Issues and Comments:
One has to login with the Stanford website to use the material.
Creative Commons:
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