Module Objectives#

The goal of this module is to introduce learners to the fields of machine learning and deep learning. By the end of this module, learners should understand how predictive models are built in Python and be able to distinguish between simple machine learning models, such as linear regression, and deep learning models. Learners will also gain an appreciation for how data is used to build ML models, the process of developing ML models and deploying them to production, and the infrastructure required to support ML systems.

Module Topics#

• Machine Learning (ML) and Neural Networks

  • Problem formulation and techniques: Regression, Nearest Neighbors, Tree-Based Models, Clustering, Principal Component Analysis.

• Major ML Application Areas

  • Natural Language Processing (NLP), Computer Vision, Recommender Systems.

• Platforms for Building ML Models

  • Python for ML and Data Science.

• Machine Learning vs. Statistics

  • Similarities and Differences.

• Tools and Platforms

  • Python, scikit-learn, PyTorch, and cloud-based platforms.

• Building ML Systems

  • Data preparation, model training and evaluation, model deployment, and serving.

ML Use Cases#

Practical Labs#

• Traditional ML

  • Build a predictive model to replace/impute missing data.

  • Build a predictive model for predicting poverty from LSM data.

• Deep Learning

  • Build a simple computer vision model.

  • Deep Learning-NLP: Build a document classification system.

Case Studies#

• [World Bank] Small area estimation of poverty.

• [World Bank] Object detection from high-resolution satellite imagery.

Assessment#

• To be determined (TBD).