Teaching

2021 : Cours + TD (16h) Complexité des Algorithmes, Level: L2. Place: Université Paul Sabatier

TP (16h) Apprentissage Automatique, Level: M1, Place: Université Paul Sabatier

2018-2019 : Cours magistral (CM) on Introduction to data sciences.

Level L2, L3 and M1. Place: Peking University.

Lecture Notes:

  1. Data pre-processing (note 1, note 2, note 2 math, note 3).
  2. Classification model (note 1, note 2)
  3. Dimensionality reduction (note)
  4. Text and Graph analysis (note 1, note 2)
  5. Deep learning (note)
  6. Distributed computing (note)

Kaggle-class Challenges:

https://www.kaggle.com/c/traffic-prediction-2019

https://www.kaggle.com/c/flower-classification-2019

https://www.kaggle.com/c/pes-challenge-2019

2011 : Travaux Dirigés (TD) on Data Mining with Prof. Zvi Kedem.

Level M1 and M2. Place: New York University.

Notes:

  1. Homework on linear and logistic regression (note)
  2. Note on Naive Bayes in Weka software (note)
  3. Note on how to use Weka (note)
  4. Note on linear and logistic regression (note)
  5. Lecture note on EM algorithm (note)

Thème : Overlay par Kaira. Texte supplémentaire
Le Cap, Afrique du sud