Artificial intelligence (AI) and machine learning (ML) are about creating intelligent systems – systems that perceive and respond to the world around them. AI and ML systems are everywhere, in our cars and smartphones, and businesses of all sizes are investing in these areas.

The AI/ML concentration combines a rigorous computer science degree with coursework in AI, ML, and big data. This concentration also provides students the necessary foundational coursework and skills in math, statistics, and data science.

Learning Outcomes

Upon completing this program, students will be able to:

  • Develop AI and ML approaches for complex real-world problems.
  • Deploy high-performance computing tools for the analysis of large datasets.
  • Use a broad range of AI and ML tools, techniques, and algorithms.
  • Apply AI and ML tools in an ethical and socially responsible manner, with an awareness of biases that can result from their indiscriminate use.
  • Communicate results of complex analyses verbally and in writing using appropriate visualization techniques.
  • Confidently pursue graduate studies or professional employment in AI/ML and computer science.

Potential Occupations

In addition to the career opportunities open to all computer science graduates, the AI/ML concentration opens career paths that include:

Machine learning engineer, data scientist, business intelligence developer, big data engineer, data mining analyst, natural language processing analyst, computer vision engineer.

Effective Fall 2020

A minimum grade of C (2.000) is required in CO 150 and in all CS, DSCI, MATH, and STAT courses which are required for graduation .

Freshman
AUCCCredits
CO 150College Composition (GT-CO2)1A3
CS 165CS2--Data Structures 4
MATH 160Calculus for Physical Scientists I (GT-MA1)1B4
MATH 161Calculus for Physical Scientists II (GT-MA1)1B4
Select one course from the following: 4
CS1---No Prior Programming Experience  
CS1--Prior Programming Experience  
Select at least two courses totaling a minimum of 7 credits from the following (one course must be or include the sequenced laboratory): 7
Introduction to Astronomy (GT-SC2)3A 
Human Origins and Variation (GT-SC2)3A 
Principles of Animal Biology (GT-SC2)3A 
Principles of Plant Biology (GT-SC1)3A 
Fundamentals of Chemistry (GT-SC2)3A 
General Chemistry I (GT-SC2)3A 
Foundations of Modern Chemistry  
The Blue Planet - Geology of Our Environment (GT-SC2)3A 
Geology of Natural Resources (GT-SC2)3A 
Physical Geology for Scientists and Engineers3A 
Honors Seminar: Knowing in the Sciences3A 
Attributes of Living Systems (GT-SC1)3A 
Biology of Organisms-Animals and Plants (GT-SC1)3A 
Introductory Genetics: Applied/Population/Conservation/Ecological (GT-SC2)3A 
Introductory Genetics: Molecular/Immunological/Developmental (GT-SC2)3A 
Fundamentals of Ecology (GT-SC2)3A 
Oceanography (GT-SC2)3A 
General Physics I (GT-SC1)3A 
General Physics II (GT-SC1)3A 
Physics for Scientists and Engineers I (GT-SC1)3A 
Physics for Scientists and Engineers II (GT-SC1)3A 
Arts and Humanities3B3
Elective 1
 Total Credits 30
Sophomore
 
CS 201/PHIL 201Ethical Computing Systems (GT-AH3)3B3
CS 220Discrete Structures and their Applications 4
CS 253Software Development with C++ 4
CS 270Computer Organization 4
Select one course from the following: 3-4
Linear Algebra for Data Science  
Linear Algebra I  
Select one course from the following: 3
Introduction to Applied Statistical Methods  
Introduction to Biostatistics  
Intro to Theory and Practice of Statistics  
Diversity and Global Awareness3E3
Historical Perspectives3D3
Electives 2-3
 Total Credits 30
Junior
 
CS 314Software Engineering4A,4B3
CS 320Algorithms--Theory and Practice 3
CS 345Machine Learning Foundations and Practice 3
CS 370Operating Systems 3
Technical Electives - select a minimum of six credits from the following: 6-8
Optimization Methods in Data Science  
Inferential Reasoning in Data Analysis  
Data Graphics and Visualization  
Introduction to Geometric Data Analysis  
Topological Data Analysis  
Statistical Data Analysis I  
Statistical Data Analysis II  
Statistical Computing  
Probability and Mathematical Statistics I  
Calculus for Physical Scientists III  
Introduction to Combinatorial Theory  
Introduction to Mathematical Modeling  
Mathematics of Information Security  
Fourier and Wavelet Analysis with Apps  
Introduction to Numerical Analysis I  
CS course numbered 300- or above, excluding 380-399 and 480-499 3-4
Advanced Writing23
Social and Behavioral Sciences3C3
Electives 0-3
 Total Credits 30
Senior
 
Capstone Courses - select two courses from the following (one of the selected courses will fulfill AUCC 4C): 8
Introduction to Bioinformatics Algorithms4C 
Introduction to Artificial Intelligence4C 
Introduction to Machine Learning4C 
Systems Elective - select one course from the following:  4
Introduction to Big Data  
Introduction to Distributed Systems  
Parallel Programming  
Additional Computer Science Course - select one course from the following: 4
Introduction to Computer Graphics  
Introduction to Bioinformatics Algorithms  
Database Systems  
Introduction to Big Data  
Introduction to Artificial Intelligence  
Introduction to Machine Learning  
Introduction to Distributed Systems  
Principles of Human-Computer Interaction  
Parallel Programming  
Electives1 14
 Total Credits 30
 Program Total Credits: 120

Distinctive Requirements for Degree Program:

To prepare for first semester: The curriculum for the Computer Science major assumes students enter college prepared to take calculus. Entering students who are not prepared to take calculus will need to fulfill pre-calculus requirements in the first semester. All students must maintain a C (2.000) or better in CO 150 and in all CS, DSCI, MATH, and STAT courses which are required for graduation.

Freshman
Semester 1CriticalRecommendedAUCCCredits
MATH 160Calculus for Physical Scientists I (GT-MA1) X1B4
Select one course from the following:   4
CS1---No Prior Programming Experience X  
CS1--Prior Programming Experience X  
Arts and Humanities  3B3
Department Approved Science (See list on Concentration Requirements Tab)  3A3
Electives   1
MATH 124 and MATH 126 may be necessary for some students to fulfill pre-calculus requirements.X   
 Total Credits   15
Semester 2CriticalRecommendedAUCCCredits
CO 150College Composition (GT-CO2)  1A3
CS 165CS2--Data Structures X 4
MATH 161Calculus for Physical Scientists II (GT-MA1)  1B4
Department Approved Science with Lab (See list on Concentration Requirements Tab)  3A4
CO 150 must be completed by the end of Semester 2 with a grade of C or better.X   
CS 163 or CS 164 must be completed by the end of Semester 2.X   
 Total Credits   15
Sophomore
Semester 3CriticalRecommendedAUCCCredits
CS 220Discrete Structures and their Applications X 4
CS 270Computer Organization X 4
Select one course from the following:   3
Introduction to Applied Statistical Methods    
Introduction to Biostatistics    
Intro to Theory and Practice of Statistics    
Historical Perspectives  3D3
Electives   1
 Total Credits   15
Semester 4CriticalRecommendedAUCCCredits
CS 201/PHIL 201Ethical Computing Systems (GT-AH3) X3B3
CS 253Software Development with C++ X 4
Select one course from the following:   3-4
Linear Algebra for Data ScienceX   
Linear Algebra IX   
Diversity and Global Awareness X3E3
Electives   1-2
CS 165 and CS 220 and CS 270 must be completed by the end of Semester 4.X   
MATH 160 and MATH 161 and MATH 369 or DSCI 369 must be completed by the end of Semester 4.X   
 Total Credits   15
Junior
Semester 5CriticalRecommendedAUCCCredits
CS 314Software Engineering X4A,4B3
CS 320Algorithms--Theory and Practice X 3
CS 370Operating Systems X 3
Advanced Writing  23
Social and Behavioral Sciences X3C3
CS 253 must be completed by the end of Semester 5.X   
 Total Credits   15
Semester 6CriticalRecommendedAUCCCredits
CS 345Machine Learning Foundations and Practice X 3
One CS course numbered 300- or above, excluding 380-399 and 480-499 X 3-4
Technical Electives (See list on Concentration Requirements Tab) X 6-8
Electives   0-3
CS 314 and CS 320 and CS 370 must be completed by the end of Semester 6.X   
 Total Credits   15
Senior
Semester 7CriticalRecommendedAUCCCredits
Capstone Course (See list on Concentration Requirements tab)X 4C4
Systems Elective (See list on Concentration Requirements tab) X 4
Electives   7
At least 2 Upper-Division CS classes must be completed by the end of Semester 7.X   
 Total Credits   15
Semester 8CriticalRecommendedAUCCCredits
Capstone Course (See list on Concentration Requirements tab)X  4
Additional Computer Science Course (See list on Concentration Requirements tab)X  4
ElectivesX  7
The benchmark courses for the 8th semester are the remaining courses in the entire program of study.X   
 Total Credits   15
 Program Total Credits:   120