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Computer Science: Machine Learning (BS)

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Computer Science Bachelors BS

Variable Credit Min

74

Variable Credit Max

74

Major Academic Plan

Title

Computational Practice:

Learning Outcome

Students will design and implement significant computer programs that meet a human need.

Title

Computational Theory

Learning Outcome

Students will analyze problems and their algorithmic solutions using theoretical concepts.

Title

Career Preparation

Learning Outcome

Students will have sufficient maturity in machine learning to work in a professional setting or enter a graduate program.

Title

Diversity, Equity, and Inclusion

Learning Outcome

Our program is accessible to everyone, including women, minorities, and those new to programming, and provides an equal opportunity for every student to succeed.

Program Requirements

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

course - Intro to Machine Learning 3.0

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 - Principles of Statistics 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

C S 575 - 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 9 — 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.