CS4641 Machine Learning



Course Description

CS4641 is an introductory survey of modern machine learning. Machine learning is an active and growing field that would require many courses to cover completely. This course aims at the middle of the theoretical versus practical spectrum. We will learn the concepts behind several machine learning algorithms wtihout going deeply into the mathematics and gain practical experience applying them. We will consider pattern recognition and artificial intelligence perspectives, making the course valuable to students interested in data science, engineering, and intelligent agent applications.

Learning Outcomes



Grade Cutoffs: A: 90, B: 80, C: 70, D: 60. No rounding.


Two or three in-class written exams, 2-5 homework assignments, and a semester-long project. Your last homework or project may be due the week preceding final exams. Assignments must be turned in before the date and time indicated as the assignment’s due date.

Class Participation

In-class exercises cannot be made up if you do not attend the class. It’s a violation of the Academic Honor Code to submit work or sign in for other students.

Academic Integrity and Collaboration

We expect academic honor and integrity from students. Please study and follow the academic honor code of Georgia Tech: http://www.honor.gatech.edu/content/2/the-honor-code. You may collaborate on homework assignments, but your submissions must be your own. You may not collaborate on in-class programming exercises or exams.

Due Dates, Late Work, and Missed Work


To contest any grade you must submit an official regrade form to the Head TA within one week of the assignment’s original return date. The original return date is the date the exam was first made available for students to pick up or the grade was posted online in the case of homework assignments and programming exercises. Note that a regrade means just that – we will regrade your assignment from scratch, which means you may end up with a lower score after the regrade.

Course Outline

This outline applies to Fall and Spring semesters. Summer schedule is compressed into 11 instructional weeks.


At least one of:


This course will include primer material on the mathematical background required for machine learning.

Course Materials


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For more details see http://www.policylibrary.gatech.edu/policy-nondiscrimination-and-affirmative-action