Mathematics and Statistics, Ph.D., Machine Learning and Data Science Concentration

Common Core Courses (12 credit hours)

Students must take at least 4 of the following common core courses:

• MATH 8110 - Real Analysis I 3 Credit Hours
• MATH 8200 - Advanced Matrix Analysis 3 Credit Hours
• MATH 8510 - Applied Mathematics 3 Credit Hours
• STAT 8561 - Linear Statistical Analysis I 3 Credit Hours
• STAT 8600 - Probability Theory 3 Credit Hours
• MATH 9116 - Teaching College Mathematics 3 Credit Hours

Foundation Courses (15 Credits)

The Machine Learning and Data Science concentration requires that a student takes four PhD qualifying examinations covering four courses, with two from the Common Core (excluding MATH 9116), one comprehensive exam from the statistical foundation courses, and one from the mathematical foundation courses. Students must take at least 5 of the following foundation courses:

Programming Courses (12 Credits)

Students must take the following 3 programming courses:

• CSC 6780 - Fundamentals of Data Science 4 Credit Hours
• CSC 8851 - Deep Learning 4 Credit Hours

•  

• CSC 6740 - Data Mining 4 Credit Hours

• or

• CSC 6850 - Machine Learning 4 Credit Hours

Elective Courses (15 Credits)

Students must take at least 15 credit hours that should be selected from other graduate level courses in the Department of Mathematics and Statistics and courses from other departments listed below. At least 9 credit hours of these courses should be MATH or STAT courses. Courses that have been counted in the core courses or foundation courses cannot be double counted in the elective courses. The courses need to be approved by the advisor(s) and the graduate director(s).

• I. Statistical Courses

• STAT 8090 - Applied Multivariate Statistics 3 Credit Hours
• STAT 8310 - Bayesian Data Analysis 3 Credit Hours
• STAT 8440 - Survival Analysis 3 Credit Hours
• STAT 8540 - Advanced Methodologies in Biostatistics 3 Credit Hours
• STAT 8550 - Applied functional data analysis 3 Credit Hours
• STAT 8581 - Statistical Theory I 3 Credit Hours
• STAT 8582 - Statistical Theory II 3 Credit Hours
• STAT 8610 - Time Series Analysis 3 Credit Hours
• STAT 8630 - Experimental Designs 3 Credit Hours
• STAT 8670 - Computational Methods in Statistics 3 Credit Hours
• STAT 8674 - Monte Carlo Methods 3 Credit Hours
• STAT 8678 - SAS Programming and Data Analysis 3 Credit Hours
• STAT 8680 - Applied Nonparametric Methods 3 Credit Hours
• STAT 8700 - Categorical Data Analysis 3 Credit Hours
• STAT 8760 - Sample Surveys 3 Credit Hours
• STAT 8800 - Statistical Consulting I 1 Credit Hours
• STAT 8900 - Colloquium 1 Credit Hours

•  

  II. Mathematical Courses

• MATH 8200 - Advanced Matrix Analysis 3 Credit Hours
• MATH 8500 - Systems Biology 3 Credit Hours
• MATH 8505 - Advanced Mathematical Biology 3 Credit Hours
• MATH 8515 - Dynamical Foundations of Neuroscience 3 Credit Hours
• MATH 8520 - Applied Combinatorics and Graph Theory 3 Credit Hours
• MATH 8525 - Applied Stochastic Processes 3 Credit Hours
• MATH 8535 - Applied Matrix Algebra 3 Credit Hours
• MATH 8560 - Informatics of Neural and Cardiovascular Systems 3 Credit Hours
• MATH 8600 - Mathematical Foundations of Deep Learning 3 Credit Hours
• MATH 8610 - Advanced Numerical Analysis 3 Credit Hours
• MATH 8620 - Numerical Linear Algebra 3 Credit Hours
• MATH 8800 - Topics in Mathematics 3 Credit Hours
• MATH 8900 - Math Colloquium 1 Credit Hours
• STAT 8305 - Pattern Recognition in Machine Learning 3 Credit Hours
• CSC 6760 - Big Data Programming 4 Credit Hours

•  

  III. Computational Courses

• CSC 8850 - Advanced Machine Learning 4 Credit Hours
• CSC 8851 - Deep Learning 4 Credit Hours
• CSC 8852 - Advanced Topics of Deep Learning 4 Credit Hours