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Mastering Machine Learning: From Basics to Applications

Dive into the world of machine learning with this comprehensive course! Learn the internals of ML algorithms, build ML applications, and start ML research with confidence. Prerequisites include computer science principles, Python coding, probability, linear algebra, and more. Honor code emphasized. Course includes homework assignments and a final exam. Explore the history and recent progress in ML, including computer vision, autonomous driving, speech recognition, and deep reinforcement learning. Course covers supervised learning, unsupervised learning, deep learning, learning theory, and reinforcement learning. Get ready to become an ML expert!

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Mastering Machine Learning: From Basics to Applications

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  1. Machine learningCS 229 / stats 229

  2. Introduction

  3. 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)

  4. 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

  5. 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.

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

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

  8. 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!

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

  10. 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 • Probabilistic vs Non-probabilistic • Deep Learning [Learning representations] • Our focus: supervised setting • Learning Theory • Bias-Variance Tradeoff • Generalization and Uniform Convergence • Reinforcement Learning [Sequential Decision Making]

  11. Preview - Supervised

  12. PREVIEW – UNSUPERVISED (ICA) Mixed Separated

  13. PREVIEW – UNSUPERVISED (ICA - II) Mixed Separated

  14. PREVIEW – REINFORCEMENT LEARNING

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