Computer Science (BS): Machine Learning Emphasis

Grades below C- are not allowed in major courses.

Requirement 1 — Complete 10 Courses

course - Intro to Computer Science 3.0

course - Intro to Data Science 3.0

course - Computer Systems 3.0

course - Data Structures 3.0

course - Discrete Structure 3.0

course - Adv Software Construction 4.0

course - Algorithm Design & Analysis 3.0

course - Ethics & Computers in Society 2.0

C S 472- Intro to Machine Learning 3.0 - This course is no longer available.

course - Deep Learning 3.0

Requirement 2 — Complete 3 Courses

course - Calculus 1 4.0

course- Fundamentals of Mathematics 3.0

course - Mathematics of Data Science 3.0

Requirement 3 — Complete 2 Courses

course - Elementary Linear Algebra 2.0

course - Computational Linear Algebra 1.0

Requirement 4 — Complete 1 of 2 Courses

course - Intro to Stat Data Analysis 3.0

course - Stat for Engineers & Scientist 3.0

Requirement 5 — Complete 1 of 3 Courses

course - Intro to Econometrics 3.0

course - Stat Modeling for Data Science 3.0

course - Introduction to Regression 3.0

Requirement 6 — Complete 1 of 2 Courses

course - Linear Prog/Convx Optimization 3.0

course - Methods of Applied Math 2 3.0

Requirement 7 — Complete 2 of 4 Courses

course - Computer Vision 3.0

course - Intro Artificial Intelligence 3.0

course - Voice Interfaces 3.0

course - Intro to Machine Translation 3.0

Requirement 8 — Complete 6 hours

Option 8.1 — Complete up to 6 hours

course - Data Science Capstone 1 3.0

course - Data Science Capstone 2 3.0

Option 8.2 — Complete up to 6 hours

course - Undergraduate Research - You may take twice 3.0

If you complete this option you must take two semesters, totaling 6.0 credits

Requirement 9 — Complete 9 hours

Note: Courses taken to fulfill Requirements 6 and 7 cannot double count here

course - Linear Prog/Convx Optimization 3.0

course - Computer Vision 3.0

course - Database Modeling Concepts 3.0

course - Fund of Information Retrieval 3.0

course - Intro Artificial Intelligence 3.0

course - Voice Interfaces 3.0

course - Intro to Machine Translation 3.0

course - Robust Control 3.0

course - Intro to Network Science 3.0

course - Theory of Predictive Modeling 3.0

course - Statistics for Economists 3.0

course - Natural Lang Processing 3.0

course - Calculus 2 4.0

course - Calculus of Several Variables 3.0

course - Advanced Linear Algebra 3.0

course - Probability Theory 3.0

course - Probability and Inference 1 3.0

course - Intro to Bayesian Statistics 3.0

course - Probability and Inference 2 3.0

course - Data Science Process 3.0

Requirement 10 — Obtain confirmation from your advisement center that you have completed the following:

Complete Senior Exit Interview with the Computer Science department during last semester or term.

Note: Math 112, Math 113, Phscs 121, Engl 316, and C S 312 can be used to fill both General Education and program requirements. Advanced Writing and Oral Communication: Engl 316. Quantitative Reasoning: Math 112 or 113. Languages of Learning: Math 112 or 113. Physical Science: C S 312 or Phscs 121.