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