Online Doctorate Course: Introduction to Machine Learning

por Bryan Zaldivar (CSIC IFIC)

lunes 19 abr. 2021 11:00 → 13:00 Europe/Madrid

This online 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)

Outline:

lecture 1: review of ML + summary of statistics 

lecture 2: regression and overfitting control

lecture 3: bayesian learning

lecture 4: classification

hands-on session: classification

lecture 5: neural networks

hands-on session: neural nets

lecture 6: non-parametric methods

lecture 7: gaussian processes

lecture 8: unsupervised learning

Timetable:

Lecture 1: 19/04/2021, 11:00-13:00
Lecture 2: 20/04/2021  11:00-13:00
Lecture 3: 22/04/2021, 11:00-13:00
Lecture 4: 23/04/2021, 11:00-13:00
hands-on: 26/04/2021, 11:00-13:00
Lecture 5: 27/04/2021, 11:00-13:00
hands-on: 29/04/2021  11:00-13:00
Lecture 6: 30/04/2021, 11:00-13:00
Lecture 7: 04/05/2021, 11:00-13:00
Lecture 8: 05/05/2021, 11:00-13:00
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Join Zoom Meeting

https://zoom.us/j/91830259567?pwd=SHk4YzUremovUXYyRnFqRG4wNzVMZz09

Meeting ID: 918 3025 9567

Passcode: 433654

Bayesian_sequential_learning.ipynb GP_regression.ipynb hands-on classification.ipynb hands-on_neuralnets (classification).ipynb lecture1_slides_extended-overview.pdf naive_Bayes.ipynb notes_Lecture_1.pdf notes_Lecture_2.pdf notes_Lecture_3.pdf notes_Lecture_4.pdf notes_Lecture_5.pdf notes_Lecture_6.pdf notes_Lecture_7.pdf overfitting_control_polynomial-model.ipynb