ECGR 5105: Introduction to Machine Learning
University of North Carolina, Charlotte
Machine learning is a sub-field of Artificial Intelligence that gives computers the ability to learn and/or act without being explicitly programmed. This course covers the necessary theory, principles, and algorithms for machine learning. Topics include: supervised, unsupervised learning approaches (including deep learning), optimization procedures, and statistical inference.� Students digest and practice their knowledge and skills by class discussion, homework, and exams, as well as obtain in-depth experience with a particular topic through a final project.
Average GPA: 3.70
Grade distribution records: 85 students across 7 terms.
Grade distribution
| Grade | Students | Percent |
|---|---|---|
| A | 63 | 74.1% |
| B | 17 | 20.0% |
| C | 4 | 4.7% |
| W | 1 | 1.2% |
Based on 85 student grade records across 7 terms and 4 professors.
Instructors
- Hamed Tabkhivayghan 44 students, Average GPA 3.64
- Farah Deeba 28 students, Average GPA 3.82
- Andrew Willis 8 students, Average GPA 3.75
- Vinit Katariya 5 students, Average GPA 3.50