Aller à : contenu haut bas recherche
 
 
EN     FR
Vous êtes ici:   UNIL > HEC Inst. > HEC App. > SYLLABUS
 
 

Advanced Data Analysis

  • Teacher(s):   S.Scheidegger  
  • Course given in: English
  • ECTS Credits: 6 credits
  • Schedule: Spring Semester 2018-2019, 4.0h. course (weekly average)
  •  sessions
  • site web du cours course website
  • Related programmes:
    Master of Science (MSc) in Finance, Orientation Asset and Risk Management

    Master of Science (MSc) in Finance : Financial Entrepreneurship and Data Science

    Master of Science (MSc) in Finance, Orientation Corporate Finance

    Master of Science (MSc) in Management, Orientation Marketing

    Master of Science (MSc) in Management, Orientation Business Analytics

    Maîtrise universitaire ès Sciences en management, Orientation Behaviour, Economics and Evolution

    Master of Science (MSc) in Management, Orientation Strategy, Organization and Leadership

 

Objectives

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.

Contents

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.

References

  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

Pre-requisites

  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

First attempt

Exam:
Without exam (cf. terms)  
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.

Retake

Exam:
Without exam (cf. terms)  
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.



[» go back]           [» courses list]
 
Search


Internef - CH-1015 Lausanne - Suisse  -   Tél. +41 21 692 33 00  -   Fax +41 21 692 33 05
Swiss University