(3 units). Basic theory of machine learning. Application to engineering problems. Supervised and unsupervised learning. Neural networks. Convolutional neural nets. Large language models. Tools for developing machine learning models and software applications that incorporate machine learning. Quality assurance and operations (MLOps) of machine learning systems. Feature engineering and prompt engineering. Case studies in various domains. Course Component: Laboratory, Lecture Prerequisites: MAT 1341, MAT 2377 or (MAT 2371, MAT 2375), SEG 2105 and 6 university course units in CEG or SEG at the 3000 level. Courses CEG 4195, CSI 4145, SEG 4180 cannot be combined for units.
(3 units). Basic theory of machine learning. Application to engineering problems. Supervised and unsupervised learning. Neural networks. Convolutional neural nets. Large language models. Tools for developing machine learning models and software applications that incorporate machine learning. Quality assurance and operations (MLOps) of machine learning systems. Feature engineering and prompt engineering. Case studies in various domains. Course Component: Laboratory, Lecture Prerequisites: MAT 1341, MAT 2377 or (MAT 2371, MAT 2375), SEG 2105 and 6 university course units in CEG or SEG at the 3000 level. Courses CEG 4195, CSI 4145, SEG 4180 cannot be combined for units.
