Theories for Intelligence - Lorenzo Rosasco: Learning Theory

Explore the foundations of machine learning and learning theory in this two-part lecture series delivered by Lorenzo Rosasco. Dive into supervised learning concepts, focusing on the Nearest Neighbor algorithm and its variations. Examine data sets, the importance of proximity in classification, and methods for choosing optimal values. Analyze intermediate objects, review splits, and engage with practical exercises to reinforce understanding. Investigate global and smoothness properties, and uncover the significance of Yogi Squares in the learning process. Gain valuable insights into the underlying principles that drive intelligent systems and their decision-making processes.

Intro
Machine Learning
Learning Theory Part 1
Supervised Learning
Nearest Neighbor
Data Set
The Captain
The Knearest Neighbor
Closer Points Count More
How Do You Choose
Optimal Value
Equations
Intermediate object
Review Split
Exercise
Plot it in
Algorithm
Global and Smoothness
Yogi Squares
What is this doing