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Data mining, Machine Learning

Teacher Nicolas Pasquier

The course provides the students with practical hands-on experience in data mining and machine learning using open source machine learning libraries such as sci-kit-learn in Python programming language. After completing the course, the students will be able to apply and use various data mining and machine-learning techniques on real-word big/business datasets.

The course provides knowledge of various concepts, techniques, and methods related to data mining, machine learning and deep learning approaches. Furthermore, it introduces :

  • Basics of Data mining and machine learning
  • Strengths and weaknesses of Dimensionality Reduction Algorithms: variance thresholds, Correlation Thresholds, Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA)
  • Linear models for regression such as maximum likelihood, sequential learning, regularized least squares
  • Linear models for classification such as linear classification, logistic regression, support vector machines
  • Classification models such as probabilistic generative models, probabilistic discriminative models
  • Unsupervised learning: clustering, probabilistic clustering, Expectation-Maximization Algorithm.
  • Neural Networks: feed-forward neural networks, network training, backpropagation, convolutional neural networks
  • Deep Learning: deep feed-forward networks, regularization for deep learning, optimization for training deep models, application of deep learning

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