Machine Learning for Software Engineering

This course deals with the problem of extracting information and knowledge from data, using unsupervised/supervised learning, as well as natural language processing algorithms, and how to use such knowledge to address various software engineering tasks. The course will cover the following topics:

• Data pre-processing
• Unsupervised learning
  • Hierarchical clustering, k-means, feature maps, graph-based clustering, density based clustering, anomaly detection
• Supervised learning
  • Classifiers (e.g., nearest neighbour, decision trees, naive Bayes, SVM), regression models, deep neural networks
• Evaluation methods and metrics, statistical tests
• Latent semantic indexing and latent Dirichlet allocation
• Text embedding for text search
• Part of speech tagging
• Constituency and dependency parsing
• Semantic role labelling
• Text summarization
• Sentiment analysis
• Language models

In presence