Machine Intelligence Track 2019

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 CS core courses, all track requirements, 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 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 47100	Introduction to Artificial Intelligence
CS 47300	Web Information Search & Management
CS 48300	Introduction To The Theory Of Computation
CS 49000-HCI or CS 49000-LDA or CS 49000-IDV	Human-Computer Interactions or Large-Scale Data Analysis or Introduction to Data Visualization
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.