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.
Technical Requirements:
A typical Web browser.
Need to register with the stanford website to assess this course
Identify Major Learning Goals:
his 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).
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.
Any student who needs to understand the theoretical concept of advanced statistics.
Prerequisite Knowledge or Skills:
The students should have undergone a course in basic statistics, algebra, and programming
Content Quality
Rating:
Strengths:
The materials are comprehensive and accurate.
The information is sufficient.
The concepts are clearly illustrated.
Concerns:
Not accessible until "signed up" for the course
Potential Effectiveness as a Teaching Tool
Rating:
Strengths:
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.
The students will understand advanced statistical techniques and how they work.
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.
Creative Commons:
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