ITCS 3156: Introduction to Machine Learning
University of North Carolina, Charlotte
Introduction to the machine learning pipeline of data collection, feature creation, algorithms, and evaluation for classification and regression based on the fundamental foundations on Linear Algebra, Probability Theory, and Optimization. The course covers basic concepts, such as training, validation, overfitting, and error rates in addition to commonly used machine learning algorithms, such as linear regression, perceptrons, naive Bayes, logistic regression, neural networks, dimensionality reduction, clustering, and reinforcement learning.
Average GPA: 2.89
Grade distribution records: 300 students across 3 terms.
Grade distribution
| Grade | Students | Percent |
|---|---|---|
| A | 124 | 41.3% |
| B | 76 | 25.3% |
| C | 48 | 16.0% |
| D | 19 | 6.3% |
| F | 23 | 7.7% |
| W | 10 | 3.3% |
Based on 300 student grade records across 3 terms and 4 professors.
Instructors
- Minwoo Lee 132 students, Average GPA 2.47
- Aileen Benedict 78 students, Average GPA 3.45
- Hongfei Xue 70 students, Average GPA 3.06
- Zhaocong Yang 20 students, Average GPA 2.82