Machine Learning

ECTS : 6

Lecturer in charge : Fabrice ROSSI (https://www.ceremade.dauphine.fr/en/members/detail-cv/profile/fabrice-rossi.html)

Language of instruction : French

Teaching hours : 36

Course content:

The course gives a thorough presentation of the machine learning field and follows this outline:

1. general introduction to machine learning and to its focus on predictive performances (running example: k-nearest neighbours algorithm)
2. machine learning as automated program building from examples (running example: decision trees)
3. machine learning as optimization:
   1. empirical risk minimization
   2. links with maximum likelihood estimation
   3. surrogate losses and extended machine learning settings
   4. regularisation and kernel methods (support vector machines)
4. reliable estimation of performances:
   1. over fitting
   2. split samples
   3. resampling (leave-one-out, cross-validation and bootstrap)
   4. ROC curve, AUC and other advanced measures
5. combining models:
   1. ensemble techniques
   2. bagging and random forests
   3. boosting
6. unsupervised learning:
   1. clustering (hierarchical clustering, k-means and variants, mixture models, density clustering)
   2. outlier and anomaly detection

Skills to be acquired:

After attending the course the students will

• have a good understanding of the algorithmic and statistical foundations of the main machine learning techniques
• be able to select machine learning techniques adapted to a particular task (exploratory analysis with clustering methods, predictive analysis, etc.)
• be able to design a model selection procedure adapted to a particular task
• report the results of a machine learning project with valid estimation of the performances of their model

Mandatory prerequisites

• intermediate level in either Python or R. Students are expected to be able to perform standard data management tasks in Python or R, including, but not limited to:
  • loading a data set from a CSV file
  • recoding and cleaning the data set 
  • implementing a simple data exploration strategy based on pivot table and on graphical representation
• intermediate level in statistics and probability. Students are expected to be familiar with:
  • descriptive statistics
  • conditional probabilities and conditional expectations
  • core results from statistics: bias and variance concepts, strong law of large numbers, central limit theorem, etc. 

Assessment:

• quizzes and tests during the course
• machine learning project