CSE 6520
Transcript Abbreviation:
FNDS APPLIED AI
Course Description:
Introduction to computer programming, to problem solving techniques using computer programs, and to the mathematical foundations of Artificial Intelligence. Specifically geared towards graduate students from non-Computer Science backgrounds with examples drawn from Artificial Intelligence.
Course Levels:
Graduate
Designation:
Elective
General Education Course
(N/A)
Cross-Listings
(N/A)
Credit Hours (Minimum if “Range”selected):
3.00
Max Credit Hours:
3.00
Select if Repeatable:
Off
Maximum Repeatable Credits
(N/A)
Total Completions Allowed
(N/A)
Allow Multiple Enrollments in Term:
No
Course Length:
14 weeks (autumn or spring)
Off Campus:
Never
Campus Location:
Columbus
Instruction Modes:
In Person (75-100% campus; 0-24% online)
Prerequisites and Co-requisites
(N/A)
Electronically Enforced:
No
Exclusions:
Not open to students enrolled in CSE major.
Course Goals / Objectives:
Be competent with the usage of basic components of a high-level programming language (e.g. variables, types, flow control, functions)
Be competent with the usage of common data structures of a high-level programming language (e.g. lists, tuples, maps)
Be competent with the usage of libraries in a high-level programming language
Be familiar with some basic linear algebra concepts (e.g. PCA, eigenvalues, eigenvectors) and how to use them in a high-level programming language
Be familiar with fitting statistical models to data in a high-level programming language
Be familiar with basic plotting techniques in a high-level programming language
Be exposed to basic neural networks and their usage in a high-level programming language
Be exposed to basic data analytic experimental techniques and standards
Check if concurrence sought:
No
Contact Hours:
| Topic | LEC | REC | LAB | LAB Inst |
|---|---|---|---|---|
| Basic concepts | 3.0 | 0.0 | 0.0 | 0 |
| Data structure basics | 3.0 | 0.0 | 0.0 | 0 |
| Arrays of multiple dimensions | 3.0 | 0.0 | 0.0 | 0 |
| Dataframes and Basic Plots | 6.0 | 0.0 | 0.0 | 0 |
| Linear Algebra Basics | 6.0 | 0.0 | 0.0 | 0 |
| Regression | 3.0 | 0.0 | 0.0 | 0 |
| Probabilistic modelling | 9.0 | 0.0 | 0.0 | 0 |
| Neural Network Basics | 6.0 | 0.0 | 0.0 | 0 |
| Project Discussion/Midterm | 3.0 | 0.0 | 0.0 | 0 |
| Total | 42 | 0 | 0 | 0 |
Grading Plan:
Letter Grade
Course Components:
Lecture
Grade Roster Component:
Lecture
Credit by Exam (EM):
No
Grades Breakdown:
| Aspect | Percent |
|---|---|
| Homeworks | 50% |
| Midterm | 20% |
| Final Project | 30% |
Representative Textbooks and Other Course Materials:
| Title | Author | Year |
|---|---|---|
| Python for Data Analysis | Wes McKinney | |
| Python Data Science Handbook | Jake VanderPlas | |
| Practical Statistics for Data Scientists | Bruce & Gedeck |
ABET-CAC Criterion 3 Outcomes
(N/A)
ABET-ETAC Criterion 3 Outcomes
(N/A)
ABET-EAC Criterion 3 Outcomes:
| Outcome | Contribution | Description |
|---|---|---|
| No outcome selected | ||
Embedded Literacies Info
(N/A)
Attachments
(N/A)
Additional Notes or Comments
(N/A)