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Advanced Data Analysis

  • Enseignant(s):   S.Scheidegger  
  • Titre en français: Analyse de données avancée
  • Cours donné en: anglais
  • Crédits ECTS: 6 crédits
  • Horaire: Semestre de printemps 2019-2020, 4.0h. de cours (moyenne hebdomadaire)
  •  séances
  • site web du cours site web du cours
  • Formations concernées:
    Maîtrise universitaire ès Sciences en finance, Orientation gestion des actifs et des risques

    Maîtrise universitaire ès Sciences en finance, Orientation finance d'entreprise

    Maîtrise universitaire ès Sciences en finance : Entrepreneuriat financier et science des données

    Maîtrise universitaire ès Sciences en management, Orientation marketing

    Maîtrise universitaire ès Sciences en management, Orientation business analytics

    Maîtrise universitaire ès Sciences en management, Orientation stratégie, organisation et leadership

    Maîtrise universitaire ès Sciences en management, Orientation comportement, économie et évolution

[attention] Le syllabus du cours est entrain d'être modifié par le professeur responsable. Veuillez consulter cette page à nouveau dans quelques jours. --- A titre informatif uniquement, voici l'ancien syllabus :

Objectifs

The objective of the course is to gain practical familiarity with current computer-aided data analysis and machine-learning approaches.

We will study methods from

a) supervised machine learning (classification and regression)

b) unsupervised machine learning

c*) reinforcement learning (if time permits)

A substantial part of the course will be devoted to learning on how to apply the current implementation frameworks TensorFlow, Keras, and scikit-learn and how to dispatch them on cloud-computing infrastructures by using Python.

In addition, we will have visitors from the financial and insurance industry holding guest lectures to demonstrate where these methods are applied in real-world settings.

Contenus

This course will be beginning with basic topics such as classification and linear regression and ending up with more recent topics from Deep Learning.


Some of the topics we will cover are:

  • Linear Regression
  • Classification
  • Cross-Validation, MSE, BIAS
  • performance measures
  • Regularization, Model Selection,
  • Nonlinear regression
  • Bayesian methods (e.g. Naive Bayes)
  • Tree-based methods
  • Support-vector machines
  • Gaussian process regression and classification
  • K-means, Mixture Models, and the EM Algorithm
  • Dimension reduction (PCA, active subspace)
  • Neural nets /Deep Learning

The hands-on examples will all be in Python.

Références

  1. An Introduction to Statistical Learning (with Applications in R), James, Witten, Hastie, Tibshirani, Springer
  2. Deep Learning, Goodfellow, Bengio, Courville, MIT Press
  3. Pattern Recognition and Machine Learning, Bishop, Springer
  4. Introduction to Machine Learning, Third Edition, Alpaydin, MIT Press

Pré-requis

  1. Basic econometrics
  2. Basic programming (in Python)
  3. Basic calculus and probability


The course will consist of both lectures, software tutorials, and exercises. For the tutorials and exercises, you will need to bring a laptop computer to each class. If you do not have a laptop computer, you can still follow the class but please contact the professor to help you find a solution for effective participation in the practical hands-on exercises.

Evaluation


 

1ère tentative


 
Examen:
Sans examen (cf. modalités)  
Evaluation:

There will be four graded problem sets (counting each 15% of the final grade) and a term paper of 10 pages length (counting 40% of the final grade).


We will award the grades based on whether problem set grades are generally on par with the class average and whether the final project demonstrates an understanding of the material. There will be no exams.


 

Rattrapage


 
Examen:
Sans examen (cf. modalités)  
Evaluation:

There will be four graded problem sets (counting each 15% of the final grade) and a term paper of 10 pages length (counting 40% of the final grade).


We will award the grades based on whether problem set grades are generally on par with the class average and whether the final project demonstrates an understanding of the material. There will be no exams.



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