Machine Learning

Andrew Ng
  • 3.9（157則評分）
  • 科技
Machine Learning

This course provides a broad introduction to machine learning and statistical pattern recognition. The course also discusses recent applications of machine learning, such as to robotic control, data mining, autonomous navigation, bioinformatics, speech recognition, and text and web data processing. 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.

  1. 2008/07/23 · 影片

    1. Machine Learning Lecture 1

    Lecture by Professor Andrew Ng for Machine Learning (CS 229) in the Stanford Computer Science department. Professor Ng provides an overview of the course in this introductory meeting.

    4 秒

  2. 2008/07/23 · 影片

    2. Machine Learning Lecture 2

    Lecture by Professor Andrew Ng for Machine Learning (CS 229) in the Stanford Computer Science department.

    4 秒

  3. 2008/07/23 · 影片

    3. Machine Learning Lecture 3

    science, math, engineering, computer, technology, robotics, algebra, locally, weighted, logistic, regression, linear, probabilistic, interpretation, Gaussian, distribution, digression, perceptron

    4 秒

  4. 2008/07/23 · 影片

    4. Machine Learning Lecture 4

    Lecture by Professor Andrew Ng for Machine Learning (CS 229) in the Stanford Computer Science department. Professor Ng lectures on Newton's method, exponential families, and generalized linear models and how they relate to machine learning.

    4 秒

  5. 2008/07/23 · 影片

    5. Machine Learning Lecture 5

    Lecture by Professor Andrew Ng for Machine Learning (CS 229) in the Stanford Computer Science department. Professor Ng lectures on generative learning algorithms and Gaussian discriminative analysis and their applications in machine learning.

    4 秒

  6. 2008/07/23 · 影片

    6. Machine Learning Lecture 6

    Lecture by Professor Andrew Ng for Machine Learning (CS 229) in the Stanford Computer Science department. Professor Ng discusses the applications of naive Bayes, neural networks, and support vector machine.

    4 秒

  7. 2008/07/23 · 影片

    7. Machine Learning Lecture 7

    Lecture by Professor Andrew Ng for Machine Learning (CS 229) in the Stanford Computer Science department. Professor Ng lectures on optimal margin classifiers, KKT conditions, and SUM duals.

    4 秒

  8. 2008/07/23 · 影片

    8. Machine Learning Lecture 8

    Lecture by Professor Andrew Ng for Machine Learning (CS 229) in the Stanford Computer Science department. Professor Ng continues his lecture about support vector machines, including soft margin optimization and kernels.

    4 秒
顯示全部 (20)

3.9
（滿分 5 顆星）
157 則評分

簡介

This course provides a broad introduction to machine learning and statistical pattern recognition. The course also discusses recent applications of machine learning, such as to robotic control, data mining, autonomous navigation, bioinformatics, speech recognition, and text and web data processing. 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.

資訊

「Stanford」的更多內容

你可能也會喜歡

若要收聽兒少不宜的單集，請登入帳號。

隨時掌握此節目最新消息

登入或註冊後，即可追蹤節目、儲存單集和掌握最新資訊。
選取國家或地區

非洲、中東和印度

亞太地區

歐洲

拉丁美洲與加勒比海地區

美國與加拿大