DATA 310: Applied Machine Learning

Course ID: DATA 310
Course Attribute: None
Title: Applied Machine Learning
Credit Hours: 3
Meeting Times: 10:10 to 12:00 MTWTh
Location: Remote Synchronous Off-Campus
Date Range: Summer II Semester 2021

Course Description

This course will focus on the technical application of machine learning algorithms, their nature, and discussions regarding the potential drawbacks and advantages of different classes of algorithms. Students entering into this course should have, at a minimum, a background in python and linear algebra. No single algorithm will be covered in great depth, and the course will place a focus on the code and implementation choices necessary for each class of algorithm. Topics covered will include data processing, regression in ML, decision trees, forests, k-nn, support vector machines, kernel SVM, k-means and hierarchical clustering, association rules, natural language processing, neural networks, and various associated approaches. Pre-requisite(s): (DATA 141 OR DATA 140 OR CSCI 140 OR CSCI 141) AND (DATA 146 OR CSCI 146)

Goals and Objectives:

• To provide students with a critical understanding of the variety of tools that can be used for machine learning.
• To develop students ability to communicate findings, analysis, and visualization skills for future courses (and jobs).
• To expose students to real-world problems that are being engaged with by contemporary problem solvers and decision makers.

Honor Code

Among our most significant traditions is the student-administered honor system. The Honor Code is an enduring tradition with a documented history that originates as far back as 1736. The essence of our honor system is individual responsibility. Today, students, such as yourself, administer the Honor pledge to each incoming student while also serving to educate faculty and administration on the relevance of the Code and its application to students’ lives.

The Pledge

“As a member of the William and Mary community, I pledge on my honor not to lie, cheat, or steal, either in my academic or personal life. I understand that such acts violate the Honor Code and undermine the community of trust, of which we are all stewards.”

Accessibility, Attendance & Universal Learning

William & Mary accommodates students with disabilities in accordance with federal laws and university policy. Any student who feels s/he may need accommodation based on the impact of a learning, psychiatric, physical, or chronic health diagnosis should contact Student Accessibility Services staff at 757-221-2509 or at sas@wm.edu to determine if accommodations are warranted and to obtain an official letter of accommodation. For more information, please see www.wm.edu/sas.

I am committed to the principle of universal learning. This means that our classroom, our virtual spaces, our practices, and our interactions be as inclusive as possible. Mutual respect, civility, and the ability to listen and observe others carefully are crucial to universal learning. Active, thoughtful, and respectful participation in all aspects of the course will make our time together as productive and engaging as possible.

Grade Categories

note .9 = .9 with bar notation

Grading Opportunities