1. Intro to AI & Machine Learning This module will provide an intro to the field of Artificial Intelligence and in particular to Machine Learning, providing the basis for the course. |
2. Feature Selection, Engineering & Visualization Using pandas, seaborn, plotly, ipwidgets and Dash we will walk through the basis of feature selection and engineering in large datasets and visualization. |
3. Hipothesis Testing - A/B Testing In this module we will use Python to do Hypothesis Testing and A/B testing using the frequentist approach, multi-arm bandits and the bayesian approach. We will also discuss in depth the validity o the p-value approach. |
4. Unsupervised Learning - Clustering This module will be devoted to unsupervised learning: clustering and hierarchical clustering. For that we will use both BigML and Python. |
5. Supervised Learning - Decision Trees This module will be devoted to Decision Trees for both classification and regression. Again BigML and Python will be used. |
6. Random Forest Random Forest is one of the most popular methods, extending Decision Trees towards a robust learner. Again we will use BigML and Python. |
7. XGBoost XGBoost is possibly the most powerful algorithm in use now for tabular data, both in regression and classification. This module will cover it at length. |
8. Regressions, lasso and ridge and logistic This module will cover traditional and contemporary regression algorithms together with logistic regression for classification. |
9. Dimensionality Reduction & Text processing This module will approach the topics of dimensionality reduction with PCA and text processing, so common today with exercises using sentiment analysis and wordclouds. |
10. Causality In machine learning we are mostly interested in prediction, however determining causality, particularly from data alone is crucial in many areas. In this session we will explore this emerging field of A.I.. |