Skip to content
lectern.tv
Mathematical Foundations of Machine Learning →0/7
1

The Geometry of Linear Equations

Gilbert Strang · 39:49

2

Transposes, Permutations & Vector Spaces R^n

Gilbert Strang · 47:41

3

Convex Functions — First/Second-Order Conditions & Epigraphs

Stephen Boyd · 1:17:14

4

Saddle-Point Interpretation, KKT Conditions & Perturbation

Stephen Boyd · 1:09:19

5

The Learning Problem

Yaser Abu-Mostafa · 1:21:27

6

Is Learning Feasible?

Yaser Abu-Mostafa · 1:16:48

7

Building makemore Part 3: Activations, Gradients, Kaiming Init & BatchNorm

Andrej Karpathy · 1:55:57

The Learning Problem

Yaser Abu-MostafaCaltech

Professor of Electrical Engineering and Computer Science

1:21:27·2012·undergraduate level
Path step 5: Formalizes inductive learning from finite samples and hypothesis spaces.

This inaugural lecture for Caltech's CS 156 introduces the core components of the learning problem, distinguishing between supervised, unsupervised, and reinforcement learning paradigms. It establishes foundational concepts for algorithmic inference.

AI & MLReinforcement LearningSupervised Learning

The Learning Problem - Introduction; supervised, unsupervised, and reinforcement learning. Components of the learning problem. Lecture 1 of 18 of Caltech's Machine Learning Course - CS 156 by Professor Yaser Abu-Mostafa. View course materials in iTunes U Course App - https://itunes.apple.com/us/course/machine-learning/id515364596 and on the course website - http://work.caltech.edu/telecourse.html

Produced in association with Caltech Academic Media Technologies under the Attribution-NonCommercial-NoDerivs Creative Commons License (CC BY-NC-ND). To learn more about this license, http://creativecommons.org/licenses/by-nc-nd/3.0/

Previous · Step 4

Saddle-Point Interpretation, KKT Conditions & Perturbation

Next · Step 6

Is Learning Feasible?

Comments

Add a comment...Sign in