PhD Course: Introduction to Machine Learning

by Bryan Zaldivar (CSIC IFIC)

Tuesday 2 May 2023, 15:00 → Wednesday 10 May 2023, 13:00 Europe/Madrid

1001-Primera-1-1-1 - Paterna. Seminario (Universe)

This course will be mostly blackboard-based, focusing on the statistical foundations of Machine Learning. Many numerical examples will be given in python notebooks, for the sake of illustration and further usefulness to the attendees, and two hands-on sessions are conceived. The required background to make the most of these lectures is, on the one hand, a good level of linear algebra, as well as familiarity with concepts of statistics (probability distributions, likelihood, Bayes theorem, etc)

Preliminary outline (it may change a little):

lecture 1: review of ML + summary of statistics 

lecture 2: regression and overfitting control

lecture 3: bayesian learning

lecture 4: classification

lecture 5 (hands-on session): classification

lecture 6: neural networks

lecture 7 (hands-on session): neural nets

lecture 8: non-parametric methods

lecture 9: gaussian processes

lecture 10: unsupervised learning

Timetable:

Lecture 1:          May 2nd (Tue), 15:00-17:00
Lectures 2 & 3: May 3rd (Wed)  11:00-13:00 & 15:00-17:00
Lectures 4 & 5: May 4th (Thu),  11:00-13:00 & 15:00-17:00
Lecture 6:          May 5th (Fri),    15:00-17:00
Lectures 7 & 8: May 8th (Mon), 11:00-13:00 & 15:00-17:00
Lecture 9:          May 9th (Tue),  15:00-17:00
Lecture 10:        May 10th(Wed),  11:00-13:00

Bayesian_sequential_learning-CORRECTED.ipynb classification_with_neuralnets.ipynb GP_regression.ipynb ML_BZ_lecture1_2023.pdf naive_Bayes-Copy2.ipynb overfitting_control_polynomial-model.ipynb polynomial_fit.ipynb