TGGS > ECE > 546
Machine Learning
Introduction of machine learning. Mathematics and statistics for machine learning. Data processing. Various machine learning models both supervise and unsupervised learning e.g. Regression, Classification, Clustering, Reinforcement learning models.
Credits : 3 (3-0-6)
Pre-Requisites : No
| Course Learning Outcomes (CLOs) : | |
|---|---|
| CLO 1 | To explain theoretical concepts in followings: • Statistics for machine learning. • Training of machine learning models. • Machine learning models for regression, classification, clustering applications. • Deep Learning models for image classification, object detection applications. • Deep Learning models for natural language processing applications. |
| CLO 2 | To use the knowledge of machine learning to actual industrial problems. |
| CLO 3 | To evaluate machine learning model and improve its performance. |
| CLO1 | CLO2 | CLO3 | |
| ELO 2 | ✓ | ||
| ELO 4 | ✓ | ✓ | ✓ |
| ELO 6 | ✓ | ✓ | |
| ELO 8 | ✓ | ✓ | ✓ |
Revision : April 2022 (090245337)
Other Revisions : July 2020