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CS229: Machine Learning. Instructor: Anand Avati, Department of Computer Science, Stanford University.

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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. Instructor: Anand Avati, Department of Computer Science, Stanford University.

This is the summer edition of CS229 Machine Learning that was offered over 2019 and 2020. CS229 provides a broad introduction to statistical machine learning (at an intermediate / advanced level) and covers 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 trade offs, practical ); and reinforcement learning among other topics. The structure of the summer offering enables coverage of additional topics, places stronger emphasis on the mathematical and visual intuitions, and goes deeper into the details of various topics. You can find more information about this course, such as lecture slides and syllabus, here. (from Stanfordonline)

Course Curriculum

  • Lecture 01 – Introduction and Linear Algebra Unlimited
  • Lecture 02 – Matrix Calculus and Probability Theory Unlimited
  • Lecture 03 – Probability and Statistics Unlimited
  • Lecture 04 – Linear Regression Unlimited
  • Lecture 05 – Perception and Logistic Regression Unlimited
  • Lecture 06 – Exponential Family and Generalized Linear Models (GLM) Unlimited
  • Lecture 07 – Gaussian Discriminant Analysis (GDA), Naive Bayes and Laplace Smoothing Unlimited
  • Lecture 08 – Kernel Methods and Support Vector Machine Unlimited
  • Lecture 09 – Bayesian Methods: Parametric and Non-parametric Unlimited
  • Lecture 10 – Deep Learning I Unlimited
  • Lecture 11 – Deep Learning II Unlimited
  • Lecture 12 – Bias and Variance, Regularization Unlimited
  • Lecture 13 – Statistical Learning Theory, Uniform Convergence Unlimited
  • Lecture 14 – Reinforcement Learning I Unlimited
  • Lecture 15 – Reinforcement Learning II Unlimited
  • Lecture 16 – K-means, Mixture of Gaussians (GMM), Expectation Maximization (EM) Unlimited
  • Lecture 17 – Factor Analysis and Evidence Lower Bound (ELBO) Unlimited
  • Lecture 18 – Principal Components Analysis (PCA), Independent Components Analysis (ICA) Unlimited
  • Lecture 19 – Maximum Entropy and Calibration Unlimited
  • Lecture 21 – Evaluation Metrics Unlimited
  • Lecture 22 – Practical Tips and Course Recap Unlimited
  • Lecture 23 – Course Recap and Wrap Up Unlimited

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