Machine Intelligence Track

Objectives

The track is designed to prepare students to work in fields related to analysis of data, including areas such as machine learning, artificial intelligence, 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.

All major required courses, all track requirements and track selectives, and their pre-requisites, regardless of department, must be completed with a grade of C or better. 

Required Courses (4)

Course	Title
CS 37300	Data Mining and Machine Learning
CS 38100	Introduction to the Analysis of Algorithms
CS 47100 or CS 47300	Artificial 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.

Electives (2)

Course	Title
CS 31100 and CS 41100	Competitive Programming 2 and 3 The combination of (CS31100 and CS41100) may be reviewed for approval as one elective on a case-by-case basis
CS 31400	Numerical Methods
CS 34800	Information Systems
CS 35200	Compilers: Principles And Practice
CS 44800	Introduction To Relational Database Systems
CS 45600	Programming Languages
CS 45800	Introduction to Robotics
CS 47100	Introduction to Artificial Intelligence
CS 47300	Web Information Search & Management
CS 48300	Introduction To The Theory Of Computation
CS 43900 or CS 44000 or CS 47500	Introduction to Data Visualization or Large-Scale Data Analytics or Human-Computer Interactions
CS 57700	Natural Language Processing
CS 57800	Statistical Machine Learning

Note: Three credits of a relevant EPICS or independent study project may be used as one elective with approval from the track chair.

Note: No course can be counted both for required and elective credit. This is true for all tracks.