AML · 4 credits · Daniele Bonacorsi

Applied Machine
Learning — Basic

Lecture notes, MCQ practice, and project tracker. Written as the course progresses — Mar 05 – Apr 10, 2026.

Bologna · MSc BioinformaticsMar 05 – Apr 10, 20264 creditsExam: — + — = ?/30

22Sessions

1Attended

1With notes

not startedProject

Theory Lectures

#02Types of Machine Learning

5 Mar 2026

#03Univariate & Multivariate Linear Regression

#04Cost Functions

#05Gradient Descent & Learning Rate

#06Alternatives to Gradient Descent

#07Feature Scaling, Standardisation & Normalisation

#08Polynomial Regression & the Normal Equation

#09Classification & Logistic Regression

#10Decision Boundaries & Multiclass Classification

#11Overfitting & Regularisation

#12Regularised Linear & Logistic Regression

#13Model Selection — Train / Validation / Test Split

#14Cross-Validation

#15Bias vs Variance & Learning Curves

#16Error Metrics — Confusion Matrix, Precision, Recall, F1, ROC, AUC

⬡ Hands-on Sessions

H1Jupyter & Google Colab — setup and basicshands-on

H2Data Preparation — loading & exploring a datasethands-on

H3Data Preparation — feature selection & resamplinghands-on

H4Modelling — algorithms & performance metricshands-on

H5Modelling — model comparisonhands-on

H6Modelling — algorithm tuning & improvementshands-on

Applied MachineLearning — Basic