ECE 5307
Transcript Abbreviation:
Intro Machin Learn
Course Description:
Introduction to Machine Learning. Coverage includes linear regression, linear classification, model and feature selection, neural networks, clustering, and principle components analysis. Python will be used for implementation examples.
Course Levels:
Undergraduate (1000-5000 level)
Graduate (5000-8000 level)
Designation:
Elective
General Education Course:
(N/A)
Cross-Listings:
(N/A)
Credit Hours (Minimum if “Range”selected):
4.00
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)
Off Campus:
Never
Campus Location:
Columbus
Instruction Modes:
In Person (75-100% campus; 0-24% online)
Distance Learning (100% online)
Prerequisites and Co-requisites:
Prereq: CSE 1222 or Engr 1281.xx, and Math 2568 and Stat 3470, and enrollment in ECE major; or Grad standing.
Electronically Enforced:
No
Exclusions:
Not open to students with credit for 4194.02 (Sp19, Machine Learning), 4300, 5300, or MechEng 5194 (Au19, Applied ML for MAE).
Course Goals / Objectives:
Learn how to formulate and solve linear regression problems, linear classification problems, and clustering problems.
Learn how to implement basic machine-learning tasks in Python.
Gain familiarity with model-order selection, feature selection, neural networks, and PCA.
Gain experience applying concepts from linear algebra and probability to engineering tasks.
Check if concurrence sought:
No
Contact Hours:
| Topic | LEC | REC out-of-class | REC in-class | Weekly LAB out-of-class | Weekly LAB in-class |
|---|---|---|---|---|---|
| Introduction to Machine Learning | 1.0 | 0.0 | 0 | 0.0 | 0 |
| Linear Regression | 6.0 | 0.0 | 0 | 4.0 | 0 |
| Model-order Selection and Feature Selection | 6.0 | 0.0 | 0 | 4.0 | 0 |
| Linear Classification, Logistic Regression, and Support Vector Machine | 6.0 | 0.0 | 0 | 4.0 | 0 |
| Optimization | 3.0 | 0.0 | 0 | 2.0 | 0 |
| Neural Networks and Deep Learning | 9.0 | 0.0 | 0 | 6.0 | 0 |
| Principal Components Analysis and Clustering | 6.0 | 0.0 | 0 | 4.0 | 0 |
| Total | 37 | 0 | 0 | 24 | 0 |
Grading Plan:
Letter Grade
Course Components:
Lecture
Lab
Grade Roster Component:
Lecture
Credit by Exam (EM):
No
Grades Breakdown:
| Aspect | Percent |
|---|---|
| Homework | 19% |
| Labs | 19% |
| Two midterm exams | 38% |
| Final project | 19% |
| Participation | 5% |
Representative Textbooks and Other Course Materials:
| Title | Author | Year |
|---|---|---|
| An Introduction to Statistical Learning | G. James, D. Witten, T. Hastie, and R. Tibshirani | |
| Machine Learning with Python Cookbook | C. Albon | |
| Deep Learning with PyTorch | E. Stevens and L. Antiga | |
| Hands-On Machine Learning with Scikit-Learn, Keras & TensorFlow | A. Geron |
ABET-CAC Criterion 3 Outcomes:
(N/A)
ABET-ETAC Criterion 3 Outcomes:
(N/A)
ABET-EAC Criterion 3 Outcomes:
| Significant contribution (7+ hours) | 1 | an ability to identify, formulate, and solve complex engineering problems by applying principles of engineering, science, and mathematics |
| Some contribution (1-2 hours) | 5 | an ability to function effectively on a team whose members together provide leadership, create a collaborative and inclusive environment, establish goals, plan tasks, and meet objectives |
Embedded Literacies Info: