Training agenda
  1. Introduction to ML
    • Definition of ML
    • ML vs. artificial intelligence
    • Applications of ML
    • Skills needed in ML
    • Structured/unstructured data
    • Features/instances/labels/predicted variable
    • Classification/regression/clustering
    • Types of ML
    • Training/prediction/evaluation
    • Splitting data into training and test sets
    • Linear regression
    • Logistic regression
    • Algorithm vs. model
    • ML workflow
    • ML Frameworks
    • Hello world of ML – iris classification
  2. Data preparation
    • Problems with data
    • Basics of data exploration and visualization
    • Feature types
    • Feature encoding
    • Standardization and scaling
    • Outliers
    • 3-sigma rule
    • Dealing with missing data
    • Feature selection/dimensionality reduction
  3. Selected problems in ML
    • Model evaluation
    • Overfitting and underfitting
    • Randomness and reproducibility
    • Cross-validation
    • Parameters vs. hyperparameters
    • Hyperparameter optimization
  4. Classical ML algorithms
    • Taxonomy of ML algorithms
    • K-nearest neighbors algorithm
    • Decision tree
    • Ensembling
    • Random forest
    • Multiclass and multilabel classification
    • Clustering: k-means algorithm
  5. Artificial neural networks
    • Motivation and biological inspiration
    • Neuron model
    • Activation functions
    • Multilayer perceptron – architecture
    • Multilayer perceptron – prediction
    • Multilayer perceptron – learning (backpropagation)
    • Image encoding
    • Batch learning
    • Types of neural networks
    • Interpretability of model
    • Further learning
    • Course summary: questions/comments/discussion