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<h2>Course Title</h2>

<p>Prediction and Machine Learning</p>

<h2>Course Instructor(s)</h2>

<p><a href="http://biostat.jhsph.edu/%7Ejleek/">Jeff Leek</a> </p>

<h2>Course Description</h2>

<p>One of the most common tasks performed by data scientists and data analysts are prediction and machine learning. This course will cover the basic components of building and applying prediction functions with an emphasis on practical applications. The course will provide basic grounding in concepts such as training and tests sets, overfitting, and error rates. The course will also introduce a range of model based and algorithmic machine learning methods including regression, classification trees, Naive Bayes, and random forests. The course will cover the complete process of building prediction functions including data collection, feature creation, algorithms, and evaluation. </p>

<h2>Course Content</h2>

<li>Prediction study design</li>

<li>Types of errors</li>

<li>Types of objective functions</li>

<li>Cross-validation</li>

<li>Fitting models in R (caret package)</li>

<li>Predicting with regression</li>

<li>Predicting with trees</li>

<li>Predicting with boosting and bagging</li>

<li>Combining predictors</li>

<li>Diagnosing issues</li>

<h2>Lecture Materials</h2>

<p>Lecture videos will be released weekly and will be available for the week and thereafter. You are welcome to view them at your convenience. Accompanying each video lecture will be a PDF copy of the slides and a link to an HTML5 version of the slides. </p>

<h2>Weekly quizzes</h2>

<h3>Quiz 1</h3>

<p>Assigned: Class open (1st of Month)

Due: 7th of the Month 12:00 AM UTC</p>

<h3>Quiz 2</h3>

<p>Assigned: 8th of the Month 12:01 AM UTC

Due: 14th of the Month 12:00 AM UTC</p>

<h3>Quiz 3</h3>

<p>Assigned: 15th of the Month 12:01 AM UTC

Due: 21st of the Month 12:00 AM UTC</p>

<h3>Quiz 4</h3>

<p>Assigned: 22nd of the Month 12:01 AM UTC

Due: 28th of the Month 12:00 AM UTC</p>

<h2>Background lectures</h2>

<p>Background lectures about the content of the course with respect to other quantitative courses, course logistics, and the R programming language are provided as reference material. It is not necessary to watch the videos to complete the course, however they may be useful for explaining background, the grading schemes used, and how to use R. </p>

<h2>Quiz Scoring</h2>

<p>You may attempt each quiz up to 2 times. Only the score from your final attempt will count toward your grade.</p>

<h2>Hard deadlines and soft deadlines</h2>

<p>The reported due date is the soft deadline for each quiz. You may turn in quizzes up to two days after the soft deadline. The hard deadline is the Tuesday after the Quiz is due at 23:30 UTC-5:00. Each day late will incur a 10% penalty, but if you use a late day, the penalty will not be applied to that day.</p>

<h2>Late Days for Quizzes</h2>

<p>You are permitted 5 late days for quizzes in the course. If you use a late day, your quiz grade will not be affected. </p>

<h2>Dates for the project</h2>

<h3>Submission</h3>

<p>Assigned: Class open (1st of Month)

Due: 21st of the Month 12:00 AM UTC</p>

<h3>Review</h3>

<p>Assigned: 22nd of the Month 12:01 AM UTC

Due: 28th of the Month 12:00 AM UTC</p>

<h2>Typos</h2>

<li>We are prone to a typo or two - please report them and we will try to update the notes accordingly. </li>

<li>In some cases, the videos may still contain typos that have

been fixed in the lecture notes. The lecture notes represent

the most up-to-date version of the course material.</li>

<h2>Differences of opinion</h2>

<p>Keep in mind that currently data analysis is as much art as it is science - so we may have a difference of opinion - and that is ok! Please refrain from angry, sarcastic, or abusive comments on the message boards. Our goal is to create a supportive community

that helps the learning of all students, from the most advanced

to those who are just seeing this material for the first time. </p>

<h2>Peer Review</h2>

<p>For many of the course projects, peer scoring will be necessary

to evaluate the completion of the assignments. We have created

and tested rubrics for each assignment. They are not perfect

and will not be perfectly applied. However, we believe that

the feedback from peer assessment adds value above simple multiple choice assessments. </p>

<li>We have tried to make the criteria as objective as possible,

do your best to apply them to the best of your abilities. </li>

<li>If you have questions or suggestions about the rubrics, please

report them in the forum, &ldquo;Rubric Issues&rdquo;.</li>

<li>If you disagree with the scores you received through peer review, you may report those issues in the &ldquo;Grading Issues&rdquo; forum. Please note that it will be impossible for us to revise peer-grades, but we will attempt to use reports to improve future

versions of the rubric. </li>

<h2>Technical Information</h2>

<p>Regardless of your platform (Windows or Mac) you will need a high-speed Internet connection in order to watch the videos on the Coursera web site. It is possible to download the video files and watch them on your computer rather than stream them from Coursera and this may be preferable for some of you.</p>

<h3>Here is some platform-specific information:</h3>

<p><em>Windows</em></p>

<p>The Coursera web site seems to work best with either the Chrome or the Firefox web browsers. In particular, you may run into trouble if you use Internet Explorer. The Chrome and Firefox browsers can be downloaded from: _Chrome: <a href="http://www.google.com/chrome">http://www.google.com/chrome</a> _ Firefox: <a href="http://www.mozilla.org">http://www.mozilla.org</a></p>

<p><em>Mac</em></p>

<p>The Coursera site appears to work well with Safari, Chrome, or Firefox, so any of these browsers should be fine.</p>