Computer Science: Machine Learning (BS)
Variable Credit Min
Variable Credit Max
Major Academic Plan
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Learning Outcome
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Learning Outcome
Title
Learning Outcome
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Learning Outcome
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.