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CS229: Machine Learning (Stanford Univ.). Taught by Professor Andrew Ng, this course provides a broad introduction to machine learning and statistical pattern recognition.

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September 25, 2023

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Last updated:

September 25, 2023

Duration:

Unlimited Duration

FREE

This course includes:

Unlimited Duration

Badge on Completion

Certificate of completion

Unlimited Duration

Description

CS229: Machine Learning (Stanford Univ.). Taught by Professor Andrew Ng, this course provides a broad introduction to machine learning and statistical pattern recognition.

Topics include: supervised learning (generative/discriminative learning, parametric/non-parametric learning, neural networks, support vector machines); unsupervised learning (clustering, dimensionality reduction, kernel methods); learning theory (bias/variance tradeoffs; VC theory; large margins); reinforcement learning and adaptive control. The course will also discuss recent applications of machine learning, such as to robotic control, data mining, autonomous navigation, bioinformatics, speech recognition, and text and web data processing. (from see.stanford.edu)

Course Curriculum

  • Lecture 01 – An Overview of the Course Unlimited
  • Lecture 02 – Linear Regression, Gradient Descent, Normal Equations Unlimited
  • Lecture 03 – Locally Weighted Regression, Probabilistic Interpretation, Logistic Regression Unlimited
  • Lecture 04 – Newton’s Method, Exponential Family, General Linear Models Unlimited
  • Lecture 05 – Discriminative Algorithms, Generative Algorithms, Gaussian Discriminant Analysis Unlimited
  • Lecture 06 – Neural Network, Applications of Neural Network, Support Vector Machine Unlimited
  • Lecture 07 – Optimal Margin Classifier, Karush-Kuhn-Tucker Conditions, SVM Dual Unlimited
  • Lecture 08 – Kernels, Mercer’s Theorem, Soft Margin SVM, SMO Algorithm Unlimited
  • Lecture 09 – Bias/variance Tradeoff, Empirical Risk Minimization, The Union Bound Unlimited
  • Lecture 10 – The Concept of ‘Shatter’ and VC Dimension, Model Selection, Feature Selection Unlimited
  • Lecture 11 – Bayesian Statistics and Regularization, Online Learning Unlimited
  • Lecture 12 – The Concept of Unsupervised Learning Unlimited
  • Lecture 13 – Mixture of Gaussian, Mixture of Naive Bayes, Factor Analysis Unlimited
  • Lecture 14 – The Factor Analysis Model, 0 EM for Factor Analysis Unlimited
  • Lecture 15 – Latent Semantic Indexing, Independent Component Analysis (ICA) Unlimited
  • Lecture 16 – Applications of Reinforcement Learning, Markov Decision Process (MDP) Unlimited
  • Lecture 17 – Generalization to Continuous States, Discretization, Fitted Value Iteration Unlimited
  • Lecture 18 – Finite Horizon MDPs, The Concept of Dynamical Systems Unlimited
  • Lecture 19 – Debugging Process, Linear Quadratic Regularization, Kalman Filter Unlimited
  • Lecture 20 – Partially Observable MDPs, Policy Search, Pegasus Algorithm Unlimited

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