Progression plans for graduate students at Northern Arizona University

CS Master students

The degree requirements are written under the Details tab on https://nau.edu/school-of-informatics-computing-and-cyber-systems/ms-computer-science/

Below we list some suggested courses you take to satisfy your degree requirement, with various different specialties. GTA/GRA funding requires you to take 9 units per semester.

Check on this page to see what courses will be offered in a given semester.

Specialty in Applied Artificial Intelligence / Machine Learning

This specialty is geared towards students who want broad experience with many advanced topics related to machine learning. List of recommended classes:

• Statistics and mathematics (3 units):
  • STA570 Statistical Methods I (3 units)
• Project-based learning (6 units):
  • CS685 Graduate Research (6 units)
• Electives (21 units):
  • 15 units CS courses:
    • CS570 Advanced Intelligent Systems (3 units)
    • CS599/571 Deep Learning (3 units)
    • CS599/572 Unsupervised Learning (3 units)
    • CS550 Parallel Computing (3 units)
    • CS552 High Performance Computing (3 units)
  • 6 other units required for degree, 12 shown below to satisfy 9 unit per semester requirement for GRA/GTA:
    • INF511 Modern Regression I (3 units)
    • INF512 Modern Regression II (3 units)
    • INF504 Data Mining And Machine Learning (3 units)
    • INF503 Large Scale Data Structures (3 units)

It is recommended to take INF511 before INF512 and INF504. Example progression plan:

• Semester 1
  • STA570 Statistical Methods I (3 units)
  • CS570 Advanced Intelligent Systems (3 units)
  • INF503 Large Scale Data Structures (3 units)
• Semester 2
  • CS599/572 Unsupervised Learning (3 units)
  • INF511 Modern Regression I (3 units)
  • CS550 Parallel Computing (3 units)
• Semester 3
  • CS685 Graduate Research (3 units)
  • INF512 Modern Regression II (3 units)
  • CS550 Parallel Computing (3 units)
• Semester 4
  • CS685 Graduate Research (3 units)
  • CS599/571 Deep Learning (3 units)
  • CS552 High Performance Computing (3 units)

Specialty in Machine Learning Research

This specialty is geared towards students who want a deep understanding of machine learning research, ideal for students interested to pursue a PHD. List of recommended classes:

• Statistics and mathematics (3 units):
  • STA570 Statistical Methods I (3 units)
• Project-based learning (6 units):
  • CS685 Graduate Research (6 units)
• Thesis (6 units):
  • CS699 Thesis (6 units)
• Electives (15 units):
  • 9 units CS courses required for degree, 12 units shown below to satisfy requirement of 9 units per semester for GRA/GTA.
    • CS599/571 Deep Learning (3 units)
    • CS599/572 Unsupervised Learning (3 units)
    • CS699 Thesis (6 units)
  • 6 other units required for degree, 9 shown below to satisfy 9 unit per semester requirement for GRA/GTA:
    • INF511 Modern Regression I (3 units)
    • INF512 Modern Regression II (3 units)
    • INF504 Data Mining And Machine Learning (3 units)

It is recommended to take INF511 before INF512 and INF504. Example progression plan:

• Semester 1
  • CS685 Graduate Research (3 units)
  • STA570 Statistical Methods I (3 units)
  • CS599/571 Deep Learning (3 units)
• Semester 2
  • CS685 Graduate Research (3 units)
  • CS599/572 Unsupervised Learning (3 units)
  • INF511 Modern Regression I (3 units)
• Semester 3
  • CS699 Graduate Research (6 units)
  • INF512 Modern Regression II (3 units)
• Semester 4
  • CS699 Graduate Research (6 units)
  • INF504 Data Mining And Machine Learning (3 units)

Inf PhD

The degree requirements are written under the Details tab on https://nau.edu/school-of-informatics-computing-and-cyber-systems/phd-informatics-and-computing/

Below we list some suggested courses you take to satisfy your degree requirement, with various different specialties. GTA/GRA funding requires you to take 9 units per semester.

Check on this page to see what courses will be offered in a given semester.

Specialty in machine learning research

• Semester 1
  • INF685 Graduate Research. (instead of INF502)
  • INF503 Large-scale Data Structures and Organization.
  • INF511 Modern Regression I.
• Semester 2
  • INF685 Graduate Research.
  • INF605 Professional Communication.
  • INF512 Modern Regression II.
• Semester 3
  • INF685 Graduate Research.
  • INF504 Data Mining and Machine Learning.
  • CS572 Deep Learning.
• Semester 4
  • INF685 Graduate Research.
  • CS552 High Performance Computing.
  • CS571 Unsupervised Learning.
• Semester 5
  • INF799 Dissertation (6 units)
  • INF631 Topics in Software Engineering.
• Semester 6
  • INF799 Dissertation (6 units)
  • INF63x Another Topics class.
• etc.
• INF501 Research Methods In Informatics And Computing could also be useful.

Questions?

Ask toby.hocking@nau.edu for guidance.