Machine learning CS 229 / stats 229

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Presentation transcript:

Machine learning CS 229 / stats 229

Introduction

Goals By the end of the course, Be an expert in ML (understand the internals of ML algorithms) Be able to build ML applications (know which algorithms to use when) Be able to start ML research (read research papers)

Prerequisites Basic computer science principles Big-O notation Comfortably write non-trivial code in Python/numpy Probability (CS 109, STATS 116 etc.) Random Variables Expectations Distributions Linear Algebra & Multivariate/Matrix Calculus (MATH 51, etc.) Gradients and Hessians Eigenvalue/vector

Honor code Form study groups (strongly encouraged!) Independently write-up homework and code It is an honor code violation to intentionally refer to a previous year's assignments.

Course structure 3 Homeworks : 60% (3 x 20%) Final exam (take home) : 40%

Logistics Course website (calendar, deadlines, notes) - http://cs229.stanford.edu Piazza Gradescope

What is ml? Term “Machine Learning” coined by Arthur Samuel in 1959. Samuel Checkers-playing Program Common definition (by Tom Mitchell): Machine Learning is the study of computer algorithms that improve automatically through experience Subfield of Artificial Intelligence (AI) The hottest subfield - reinvigorated interest in AI due to deep learning!

Recent progress Computer Vision / Image Recognition Speech Recognition ImageNet Convolutional Neural Networks Autonomous driving Speech Recognition Voice assistants Language Translation Google Translate Unsupervised Translation Game Playing / Deep Reinforcement Learning ATARI AlphaGo

Course Preview Supervised learning [Input -> Output mapping] Classification vs Regression Parametric vs Non-parametric Generative vs Discriminative Probabilistic vs Non-probabilistic Unsupervised learning [Learn interesting structures in the data] Clusters vs Subspaces Deep Learning [Learning representations] Our focus: supervised setting Learning Theory Bias-Variance Tradeoff Generalization and Uniform Convergence Reinforcement Learning [Sequential Decision Making]

Preview - Supervised

PREVIEW – UNSUPERVISED (ICA) Mixed Separated

PREVIEW – UNSUPERVISED (ICA - II) Mixed Separated

PREVIEW – REINFORCEMENT LEARNING