Introduction to Machine Learning
English translation
- Week 1
Course guide and preparation: Python, arrays, and plots
Course organization, materials, and checking execution
Preparation materials
- Week 2
Rule-Based Decisions and Data
Labels, features, and decisions on new inputs
- Week 3
Differences between feature representations and models
How representation, shifts, and noise affect results
- Week 4
Measurement data shapes and missing value handling
Samples, fields, missing values, and data splitting
- Week 5
Linear Regression and Prediction Error
Coefficients, MSE, and prediction range
- Week 6
Effects of Outliers and Extrapolation
Changes in some observations and model limitations
- Week 7
Logistic Regression and Error Costs
Logits, Thresholds, and imbalanced data
- Week 8
- Week 9
Decision Trees and Generalization
Depth and Train/Test performance
- Week 10
Random Forest and Comparative Evaluation
Compare with the same split and different seeds
- Week 11
Clustering and Feature Units
K, inertia, and Standardization
- Week 12
MLP and Baseline Models
Preprocessing fitted only on Train and Network size
- Week 13
Image Classification and Misclassification
Connecting images, Labels, and probabilities
- Week 14
Noisy Inputs and Model Validation
Interpret prediction changes under the same evaluation conditions
- Week 15