Pattern Recognition ResourceS

More data beats clever algorithms, but better data beats more data.   

―  Peter Norvig

📚 Pattern Recognition course – AY 2025/2026

Prof. Enrico De Santis, Prof. Antonello Rizzi

CFU: 2 (60 hours)

The course will begin next Wednesday, March 4, 2026 with the following schedule:

Wednesday 4:00–7:00 PM, Room 23

Thursday 5:00–7:00 PM, Room 17

Please enroll to the Classroom of the PR course: https://classroom.google.com/c/ODQwODAxNzI2NDQ4?cjc=vamk2rmb

Course Code: vamk2rmb

1. Starting with Python for Machine Learning (I)

   1. Theory

   2. Practice (notebook . ipynb)

2. Elements of Probability Theory and Bayesian Estimation

   1. Theory

   2. Practice (notebook . ipynb)

3. Underfitting, Overfitting, Bias–Variance trade-off and the Hoccam Razor Criterion

   1. Theory

   2. Practice (notebook .ipynb)