Purdue University - Department of Computer Science - Machine Intelligence Track

Machine Intelligence Track

Objectives

The track is designed to prepare students to work in fields related to management and analysis of data, including areas such as machine learning, information retrieval, and data mining. The track is designed to prepare students to understand, and effectively apply in practice, the principles and techniques of data and knowledge representation, search, as well as learning and reasoning with data.

Required Courses

All track requirements, regardless of department, must be completed with a grade of C or higher.

The track has four required courses that provide general background

CourseTitle
CS 37300 Data Mining and Machine Learning
CS 38100 Introduction to the Analysis of Algorithms

CS 47100

or

CS 47300

Artifical Intelligence

or

Web Information Search & Management

STAT 41600

or

MA 41600

or

STAT 51200

Probability

or

Probability

or

Applied Regression Analysis

NOTE:

Students considering graduate work or research in this area are encouraged to take STAT/MA 41600 and STAT 41700

Machine Intelligence Track Pre-requisite Flowchart (PDF)

 

Electives

The track has two electives, chosen from the list below.

CourseTitle
CS 34800 Information Systems
CS 35200 Compilers: Principles And Practice
CS 44800 Introduction To Relational Database Systems
CS 45600 Programming Languages
CS 47100 Introduction to Artificial Intelligence
CS 48300 Introduction To The Theory Of Computation
CS 47300 Web Information Search & Management

Three credits of a relevant EPICS or independent study project may be used as one elective with track committee approval.

Neither CS 471 or CS 473 may be double counted toward the required & elective courses.

Last Updated: Aug 25, 2017 4:11 PM

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