CSE 5526
Transcript Abbreviation:
Neural Networks
Course Description:
Survey of fundamental methods and techniques of neural networks; single- and multi-layer perceptrons; radial-basis function networks; support vector machines; recurrent networks; supervised and unsupervised learning.
Course Levels:
Undergraduate (1000-5000 level)
Graduate
Designation:
Elective
General Education Course:
(N/A)
Cross-Listings:
(N/A)
Credit Hours (Minimum if “Range”selected):
3.00
Max Credit Hours:
(N/A)
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)
12 weeks (summer only)
Off Campus:
Never
Campus Location:
Columbus
Instruction Modes:
In Person (75-100% campus; 0-24% online)
Prerequisites and Co-requisites:
Prereq: 3521 or 5521.
Electronically Enforced:
No
Exclusions:
Not open to students with credit for 779.
Course Goals / Objectives:
Master basic neural network methods
Be competent with solving problems using neural network techniques
Be familiar with enough background about neural networks to take other specialty courses on neural networks
Check if concurrence sought:
No
Contact Hours:
| Topic | LEC | REC | LAB | LAB Inst |
|---|---|---|---|---|
| Introduction and McCulloch-Pitts networks | 3.0 | 0.0 | 0.0 | 0 |
| Perceptrons | 3.0 | 0.0 | 0.0 | 0 |
| Regression and least mean square algorithm | 6.0 | 0.0 | 0.0 | 0 |
| Multilayer perceptrons | 7.0 | 0.0 | 0.0 | 0 |
| Radial-basis function networks | 5.0 | 0.0 | 0.0 | 0 |
| Support vector machines | 6.0 | 0.0 | 0.0 | 0 |
| Recurrent networks | 3.0 | 0.0 | 0.0 | 0 |
| Unsupervised learning and self-organization | 2.0 | 0.0 | 0.0 | 0 |
| Applications | 2.0 | 0.0 | 0.0 | 0 |
| Current research | 3.0 | 0.0 | 0.0 | 0 |
| Exam and discussion | 2.0 | 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 | 18% |
| Projects | 30% |
| Midterm | 22% |
| Final | 30% |
Representative Textbooks and Other Course Materials:
| Title | Author | Year |
|---|---|---|
| Neural networks and learning machines | Simon Haykin |
ABET-CAC Criterion 3 Outcomes:
| Outcome | Contribution | Description |
|---|---|---|
| 1 | Significant contribution (7+ hours) | Analyze a complex computing problem and to apply principles of computing and other relevant disciplines to identify solutions. |
| 2 | Substantial contribution (3-6 hours) | Design, implement, and evaluate a computing-based solution to meet a given set of computing requirements in the context of the program’s discipline. |
| 3 | Some contribution (1-2 hours) | Communicate effectively in a variety of professional contexts. |
| 4 | Some contribution (1-2 hours) | Recognize professional responsibilities and make informed judgments in computing practice based on legal and ethical principles |
| 6 | Substantial contribution (3-6 hours) | Apply computer science theory and software development fundamentals to produce computing-based solutions. |
ABET-ETAC Criterion 3 Outcomes:
(N/A)
ABET-EAC Criterion 3 Outcomes:
| Outcome | Contribution | Description |
|---|---|---|
| 1 | Significant contribution (7+ hours) | an ability to identify, formulate, and solve complex engineering problems by applying principles of engineering, science, and mathematics |
| 2 | Substantial contribution (3-6 hours) | an ability to apply engineering design to produce solutions that meet specified needs with consideration of public health, safety, and welfare, as well as global, cultural, social, environmental, and economic factors |
| 3 | Some contribution (1-2 hours) | an ability to communicate effectively with a range of audiences - pre-2019 EAC SLO (g) |
| 4 | Some contribution (1-2 hours) | an ability to recognize ethical and professional responsibilities in engineering situations and make informed judgments, which must consider the impact of engineering solutions in global, economic, environmental, and societal contexts |
| 6 | Some contribution (1-2 hours) | an ability to develop and conduct appropriate experimentation, analyze and interpret data, and use engineering judgment to draw conclusions |
| 7 | Some contribution (1-2 hours) | an ability to acquire and apply new knowledge as needed, using appropriate learning strategies |
Embedded Literacies Info:
Attachments:
(N/A)
Additional Notes or Comments:
(N/A)
Basic Course Overview:
CSE_5526_basic.pdf
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