Applied Data Analytics

COURSE  REGISTRATION OPEN

Course in English

Course description :

Introduction:

• Course objectives
• Structure of the course
• AI, Machine Learning, Deep Learning
• Supervised, Unsupervised Learning, Semi-supervised Learning
• Classification and Regression
• Generative Models
• Examples of applications

Data Preparation, Data Formats, Feature Engineering:

• Setting up a data analysis project
• Project Goal Definition & Data Collection/Generation
• Real data versus simulated data
• Synthetic samples
• Data representations
• Data formats
• Data preparation
• Visualize data to gain insights
• Handling of missing values
• Data labelling
• Manual feature engineering

• Look at the distributions of features
• Curse of dimensionality
• Imbalanced Datasets
• Ensemble Methods
• Training, Validation and Test sets
• Cross-validation

Classic Supervised Learning:

• Use cases
• Frame the problem
• Prepare Data Vectors
• Training Models:
  • Decision Trees, Random Forests
  • Ensemble Learning: Bagging, Boosting, Stacking
  • Artificial Neural Networks (ANNs)
• Undefitting and Overfitting
• Scikit-Learn and Keras
• Fine-tune your model

Unsupervised Learning:

• Unlabelled data & Unsupervised Learning
• Data Inspection & Clustering
• Using clustering for:
  • Preprocessing
  • Semi-supervised Learning
• Anomaly detection

Convolutional Neural Networks (CNNs):

• The Convolution operation
• The Max pooling operation
• Building your network
• Data Augmentation
• Visualizing what CNNs learn

Deep Learning for sequences:

• Recurrent Neural networks
• LSTM and GRU layers
• Forecasting

Generative Models:

• Feature Representation Learning
• Density Estimation

SCHEDULE:

18-22 November 2024.  Details in this document

Email (contact pédagogique) : yvonne.becherini@apc.in2p3.fr

2 points