Apprentissage automatique et Fouille de données

• Analyse 1,2,3,4

• Algèbre 1,2,3

• Probabilité 1,2

• Recherche opérationnelle

Machine learning refers to a broad set of algorithms and related concerns for discovering patterns

in data, making new inferences based on data, and generally improving the performance of a

software system without direct programming. These methods are critical for data science. Data

scientists should understand the algorithms they apply, be able to implement them, if necessary,

and make principled decisions about their use.

Scope:

• Broad categories of machine learning approaches (e.g.,supervised and unsupervised)

• Algorithms and tools (i.e.,implementations of those algorithms) in each of the broad learning categories.

• Problems related to model expressivity as well as availability of data, and techniques

• Express formally the representational power of models learned by an algorithm, and relate that to issues such as expressiveness and overfitting.

• Exhibit knowledge of methods to mitigate the effects of overfitting and curse of dimensionality in the context of machine learning algorithms.

• Provide an appropriate performance metric for evaluating machine learning algorithms/tools for a given problem.

• Differences in interpretability of learned models.

• Solve the problem of overfitting, and unbalanced datasets.

1.    What is Machine Learning
2.    ML Block diagram
3.    Examples of Machine Learning applications

1.  Linear Regression
2.  Multiple Linear Regression

1. Training/Test Error, ect
2. Positive and Negative Class
3. Overfitting and regularization
4. Cross-validation

1. Logistic Regression
2. Naïve Bayes Classifier
3. K-Nearest Neighbors
4. Support Vector Machines
5. Decision Trees

1. K-Means Clustering
2. Hierarchical clustering
3. DBSCAN & HAC Algorithm

1. Principal Component Analysis
2. Kernel Principal Component Analysis
3. Non-Negative Matrix Factorization 
4. Singular Value Decomposition 

1. Bagging & boosting and its impact on bias and variance
2. boosting
3. Random forest
4. Gradient Boosting Machines and XGBoost

Consultez les ressources disponibles concernant ce module sur le moteur de recherche de la bibliothèque, ou accédez directement au cours de vos enseignants via la plateforme de téléenseignement de l’école « e-learn ».