Introduction to Machine Learning

Information

Aim and objective

• Develop an appreciation for data exploration and visualization
• Understand a wide variety of learning algorithms
• Understand how to apply a variety of learning algorithms and related software toolboxes to data
• Understand how to perform evaluation of learning algorithms and model selection

Outcome learning

• INSA reference data :
  • Concevoir un système d'ingénierie des données [3P]
  • Optimiser un modèle [3P]
• CNISF reference data :
  • J10P [2P]
  • J40K [1P]

Course description

• Introduction to Statistical Learning
• Optimization tools for Machine Learning
• Unsupervised learning : Principal Component analysis (PCA), Clustering methods (Agglomerative clustering , K-Means, Mixture Model)
• Classification Methods : Theory of Bayesian Decision, Logistic Regression, Support Vector Machine, over-fitting, model tuning
• Introduction to large scale learning

Prerequisites

Notion of statistics and matlab programming

Bibliography

• Christopher Bishop, Pattern Recognition and Machine Learning, 2006
• Trevor Hastie, Robert Tibshirani, Jerome Friedman, The Elements of Statistical Learning (Data Mining, Inference, and Prediction), 2009
• Richard Duda, Peter Hart, David Stork, Pattern Classification,

Assessment

• Final Exam: 50%
• Practical exam: 50%