50 thoughts on “Lecture 1 | Machine Learning (Stanford)”

  1. In 2008 he says "I am studying it for 15 years " now in 2019 I wonder should I really start this course -_-

  2. How can one get access to the assignments of the course? I am not able to access the assignments from course website as clicking on the assignment link is leading me to Piazza login page

  3. Love this teacher! It’s incredibly helpful to first explain why you’re learning a particular thing and what it’s useful for. Excellent!

  4. Machine Learning: An overview with the help of R software

  5. Imaging people dropping this vastly amazing pool of knowledge just because the lecturer says 'um' from times to times. Imaging these people being the talent recruiter, ggwp.

  6. 1 an overview of the course in this introductory meeting.

    2 linear regression, gradient descent, and normal equations and discusses how they relate to machine learning.

    3 locally weighted regression, probabilistic interpretation and logistic regression and how it relates to machine learning.

    4 Newton's method, exponential families, and generalized linear models and how they relate to machine learning.

    5 generative learning algorithms and Gaussian discriminative analysis and their applications in machine learning.

    6 naive Bayes, neural networks, and support vector machine.

    7 optimal margin classifiers, KKT conditions, and SUM duals.

    8 support vector machines, including soft margin optimization and kernels.

    9 learning theory, covering bias, variance, empirical risk minimization, union bound and Hoeffding's inequalities.

    10 learning theory by discussing VC dimension and model selection.

    11 Bayesian statistics, regularization, digression-online learning, and the applications of machine learning algorithms.

    12 unsupervised learning in the context of clustering, Jensen's inequality, mixture of Gaussians, and expectation-maximization.

    13 expectation-maximization in the context of the mixture of Gaussian and naive Bayes models, as well as factor analysis and digression.

    14 factor analysis and expectation-maximization steps, and continues on to discuss principal component analysis (PCA).

    15 principal component analysis (PCA) and independent component analysis (ICA) in relation to unsupervised machine learning.

    16 reinforcement learning, focusing particularly on MDPs, value functions, and policy and value iteration.

    17 reinforcement learning, focusing particularly on continuous state MDPs, discretization, and policy and value iterations.

    18 state action rewards, linear dynamical systems in the context of linear quadratic regulation, models, and the Riccati equation, and finite horizon MDPs.

    19 debugging process, linear quadratic regulation, Kalmer filters, and linear quadratic Gaussian in the context of reinforcement learning.

    20 POMDPs, policy search, and Pegasus in the context of reinforcement learning.

  7. Stanford University has done a great service to students and learners around the world by making this videos public. It is a commendable undertaking that helps the progress of humanity as a whole by spreading knowledge throughout the world.

  8. Does anyone have access to the problem sets? I'm able to access everything except for the problem sets.

  9. Watching this after 10 years after this video has been uploaded. The concept is still relevant and easily understandbale

  10. Very nice lecture. Contributed me a lot to document this in short. ML in 5 minutes read (https://www.dataneb.com/single-post/2018/07/09/Artificial-Intelligence-Machine-Learning-Deep-Learning-Predictive-Analytics-Data-Science)

  11. is this course still good? or should i see other courses and where can i learn how to create practicaly the algorithms with c++ or python. Thank you.

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