• Data Pre-processing & Exploration - data cleaning, normalization, feature selection, dimensionality reduction (linear and non linear)
• Review of the learning process, training/testing, model selection • Supervised learning – with basic methods such as k-nn, perceptron, naïve bayes, regression, logistic regression, SVMs
• Unsupervised learning – clustering (k-means and variants, probabilistic approaches – EM, spectral clustering, association rules.
• Web Mining: recommendations, collaborative filtering, opinion/sentiment analysis, web advertising & algorithms.
• Learning from graphs: ranking in graphs, community detection and graph clustering, applications, graph similarity/graph kernels, deep learning for graphs
. Text Mining: introduction to IR, Graph of Words, Text categorization, word embeddings, deep learning for text, deep learning for NLP.
• Review of the learning process, training/testing, model selection • Supervised learning – with basic methods such as k-nn, perceptron, naïve bayes, regression, logistic regression, SVMs
• Unsupervised learning – clustering (k-means and variants, probabilistic approaches – EM, spectral clustering, association rules.
• Web Mining: recommendations, collaborative filtering, opinion/sentiment analysis, web advertising & algorithms.
• Learning from graphs: ranking in graphs, community detection and graph clustering, applications, graph similarity/graph kernels, deep learning for graphs
. Text Mining: introduction to IR, Graph of Words, Text categorization, word embeddings, deep learning for text, deep learning for NLP.