Hands-On Machine Learning (OPT9) #

Teaching #

Marc Evrard

Class hours #

Thursday, 9:00 am to 12:15 am (nov-dec)

Prerequisite #

• Basic Python programming experience
• Bachelor-level math (calculus, linear algebra, probabilities)
• Basic Machine Learning (ML) knowledge

Summary #

A practical oriented class, where students apply ML techniques to simple illustrative examples and then to tackle competitive challenges. It will start with an introduction to present (refresh) the ML landscape. Classes will then be articulated to successively focus on the major concepts of practical ML.

Outline #

• Introduction/refresher on ML
  • What is ML
  • Types of ML systems
  • Main challenges of ML
  • Testing and validation
• Working with real data
  • Frame the problem
  • Select a performance measure
  • Create the workspace
  • Take a quick look at the data structure
• Discover and visualize the data to gain insights
  • Visualizing the Data
  • Looking for correlations
  • Experimenting with attribute combinations
• Prepare the data for processing
  • Data cleaning
  • Handling categorical attributes
  • Feature scaling
  • Transformation pipelines
• Select and train a model
  • Create a train, dev, test set
  • Training and evaluating on the training set
  • Better evaluation using cross-validation
• Fine-tune your model
  • Grid search
  • Randomized search
  • Ensemble methods
  • Analyze the best models and their errors
• All-in-one, improve task score

Assessment #

Continuous assessment (CC): 100%

• Weekly quizzes
• Practical assignments