(3 units). Advanced topics in machine learning such as deep learning including CNNs, RNNs, GANs, Diffusion models, Transformers, Deep clustering, transfer learning, domain adaptation, few-shot learning, zero-shot learning, self-supervised learning and Interpretability of ML methods. Qualitative Coding of Software Engineering Artifacts, Case Study Research, Natural Language Processing Pipelines and Lexicons, Text Similarity Measures, Information Extraction and Classification, Clustering, Supervised Machine Learning, Quantitative Evaluation of NLP and ML Techniques, Deep Learning, Word Embeddings, Transformers. This course is equivalent to COMP 5139 at Carleton University. Course Component: Lecture
(3 units). Advanced topics in machine learning such as deep learning including CNNs, RNNs, GANs, Diffusion models, Transformers, Deep clustering, transfer learning, domain adaptation, few-shot learning, zero-shot learning, self-supervised learning and Interpretability of ML methods. Qualitative Coding of Software Engineering Artifacts, Case Study Research, Natural Language Processing Pipelines and Lexicons, Text Similarity Measures, Information Extraction and Classification, Clustering, Supervised Machine Learning, Quantitative Evaluation of NLP and ML Techniques, Deep Learning, Word Embeddings, Transformers. This course is equivalent to COMP 5139 at Carleton University. Course Component: Lecture
