CSE 5523
Transcript Abbreviation:
Machine Learning
Course Description:
Introduction to basic concepts of machine learning and statistical pattern recognition; techniques for classification, clustering and data representation and their theoretical analysis.
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):
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, 5521, or 5243; and 5522, Stat 3460, or 3470; and Math 2568, 2174, 4568, or 5520H; or Grad standing.
Electronically Enforced:
No
Exclusions:
(N/A)
Course Goals / Objectives:
Master basic techniques of machine learning, including linear methods, prototype-based methods, and kernel methods
Master the statistical framework of machine learning and basic concepts, such as Bayes optimal classifier
Be competent with theoretical analysis of complexity and other properties of statistical learning techniques
Be familiar with the broad spectrum of methods for classification, regression and clustering, including boosting, spectral clustering, and other methods
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 |
|---|---|---|---|---|---|
| Basics of statistical pattern recognition | 8.0 | 0.0 | 0 | 0.0 | 0 |
| Probability and statistical inference | 9.0 | 0.0 | 0 | 0.0 | 0 |
| Bayes decision theory | 6.0 | 0.0 | 0 | 0.0 | 0 |
| Overview of techniques for regression and classification, parametric and non-parametric methods including prototype-based methods, linear and kernel methods | 5.0 | 0.0 | 0 | 0.0 | 0 |
| Analysis of statistical algorithms | 4.0 | 0.0 | 0 | 0.0 | 0 |
| Clustering | 4.0 | 0.0 | 0 | 0.0 | 0 |
| Spectral clustering | 4.0 | 0.0 | 0 | 0.0 | 0 |
| K-means algorithm | 0.0 | 0.0 | 0 | 0.0 | 0 |
| Gaussian mixture models and the EM algorithm | 0.0 | 0.0 | 0 | 0.0 | 0 |
| Empirical risk minimization and VC-theory | 0.0 | 0.0 | 0 | 0.0 | 0 |
| Generalization bounds | 0.0 | 0.0 | 0 | 0.0 | 0 |
| Dimensionality reduction | 0.0 | 0.0 | 0 | 0.0 | 0 |
| Principal Components Analysis and Multidimensional Scaling | 0.0 | 0.0 | 0 | 0.0 | 0 |
| Advanced topics in machine learning | 0.0 | 0.0 | 0 | 0.0 | 0 |
| Discussion of applications, e.g. speech, language, and vision | 0.0 | 0.0 | 0 | 0.0 | 0 |
| Total | 40 | 0 | 0 | 0 | 0 |
Grading Plan:
Letter Grade
Course Components:
Lecture
Grade Roster Component:
Lecture
Credit by Exam (EM):
No
Grades Breakdown:
| Aspect | Percent |
|---|---|
| Homework | 25% |
| Project | 40% |
| Exam | 30% |
| Participation | 5% |
Representative Textbooks and Other Course Materials:
| Title | Author | Year |
|---|---|---|
| Pattern Recognition | S. Theodoridis, K. Koutroumbas, |
ABET-CAC Criterion 3 Outcomes:
| Significant contribution (7+ hours) | 1 | Analyze a complex computing problem and to apply principles of computing and other relevant disciplines to identify solutions. |
| Substantial contribution (3-6 hours) | 2 | Design, implement, and evaluate a computing-based solution to meet a given set of computing requirements in the context of the program’s discipline. |
| Some contribution (1-2 hours) | 3 | Communicate effectively in a variety of professional contexts. |
| Some contribution (1-2 hours) | 4 | Recognize professional responsibilities and make informed judgments in computing practice based on legal and ethical principles |
| Some contribution (1-2 hours) | 5 | Function effectively as a member or leader of a team engaged in activities appropriate to the program’s discipline |
| Significant contribution (7+ hours) | 6 | 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:
| Significant contribution (7+ hours) | 1 | an ability to identify, formulate, and solve complex engineering problems by applying principles of engineering, science, and mathematics |
| Substantial contribution (3-6 hours) | 2 | 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 |
| Some contribution (1-2 hours) | 3 | an ability to communicate effectively with a range of audiences - pre-2019 EAC SLO (g) |
| Some contribution (1-2 hours) | 4 | 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 |
| 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 |
| Significant contribution (7+ hours) | 6 | an ability to develop and conduct appropriate experimentation, analyze and interpret data, and use engineering judgment to draw conclusions |
Embedded Literacies Info: