Machine Learning - Curso

1. Introduction to Machine Learning

• Definition of a process flow and Machine Learning
• Apprentice supervised and unsupervised
• Types of algorithms: clustering, classification, regression,...
• Measuring quality of an algorithm: Confusion Matrix, ROC curve (AUC).

2. Extracting data structure: Clustering

• K-Means
• Gaussian Mixture Models (GMM)

3. Recommendation Systems

• Singular value decomposition of a matrix (SVD)
• Collaborative filtering
• Recommendation using SVD (Choose movies for a user)
• Recommendation using SVD (Choose users a campaign promoting a film)

4. Classification

• Linear regression
• Logistic regression
• Fraud detection with logistic regression

5. Deep Neural Networks and Learning

• Introduction to Neural Networks
• Simple perceptron
• multilayer perceptron
• Backpropagation training algorithm (backpropagation)
• Restricted Boltzmann Machines (RBMS)

6. Election Systems

• Test A / B (Experiment 2 groups)
• Experiment slot machine with several levers

7. Natural Language Processing

• Natural Language Processing with Python (NLTK)
• Useful features for semantic analysis
• Generation vocabularies
• Tokenization: Step text phrases
• Sentiment analysis on movie reviews