• 投稿

林轩田机器学习基石(国语)

44万
8007
2017-07-22 16:15:09
4220
2180
2.4万
1916
https://www.youtube.com/playlist?list=PLXVfgk9fNX2I7tB6oIINGBmW50rrmFTqf Machine Learning Foundations (機器學習基石)
机器学习
AI
人工智能
公开课
爱可可-爱生活 发消息
新浪微博 @爱可可-爱生活 http://weibo.com/fly51fly
视频选集
（8/65）
1.1 The Learning Problem - Course Introduction
09:50
1.2 The Learning Problem - What Is Machine Learning
18:30
1.3 The Learning Problem - Applications of Machine Learning
18:58
1.4 The Learning Problem - Components of Learning
11:46
1.5 The Learning Problem - Machine Learning and Other Fields
10:22
2.1 Learning to Answer Yes_No - Perceptron Hypothesis Set
15:12
2.2 Learning to Answer Yes_No - Perceptron Learning Algorithm
19:47
2.3 Learning to Answer Yes_No - Guarantee of PLA
12:38
2.4 Learning to Answer Yes_No - Non-Separable Data
12:56
3.1 Types of Learning - Learning with Different Output Space
16:57
3.2 Types of Learning - Learning with Different Data Label
18:14
3.3 Types of Learning - Learning with Different Protocol
11:10
3.4 Types of Learning - Learning with Different Input Space
14:14
4.1 Feasibility of Learning - Learning is Impossible
13:03
4.2 Feasibility of Learning - Probability to the Rescue
11:33
4.3 Feasibility of Learning - Connection to Learning
16:47
4.4 Feasibility of Learning - Connection to Real Learning
18:07
5.1 Training versus Testing - Recap and Preview
13:19
5.2 Training versus Testing - Effective Number of Lines
15:27
5.3 Training versus Testing - Effective Number of Hypotheses
16:18
5.4 Training versus Testing - Break Point
07:44
6.1 Theory of Generalization - Restriction of Break Point
13:47
6.2 Theory of Generalization - Bounding Function - Basic Cases
06:57
6.3 Theory of Generalization - Bounding Function - Inductive
14:48
6.4 Theory of Generalization - A Pictorial Proof
16:02
7.1 The VC Dimension - Definition of VC Dimension
12:34
7.2 The VC Dimension - VC Dimension of Perceptrons
13:28
7.3 The VC Dimension - Physical Intuition of VC Dimension
06:11
7.4 The VC Dimension - Interpreting VC Dimension
17:14
8.1 Noise and Error - Noise and Probabilistic Target
16:30
8.2 Noise and Error - Error Measure
15:11
8.3 Noise and Error - Algorithmic Error Measure
13:48
8.4 Noise and Error - Weighted Classification
16:55
9.1 Linear Regression - Linear Regression Problem
09:37
9.2 Linear Regression - Linear Regression Algorithm
20:04
9.3 Linear Regression - Generalization Issue
20:35
9.4 Linear Regression - for Binary Classification
11:24
10.1 Logistic Regression - Logistic Regression Problem
14:02
10.2 Logistic Regression - Logistic Regression Error
15:59
10.3 Logistic Regression - Gradient of Logistic Regression Error
15:39
10.4 Logistic Regression - Gradient Descent
19:19
11.1 Linear Models for Classification - Binary Classification
20:59
11.2 Linear Models for Classification - Stochastic Grad. Descent
11:40
11.3 Linear Models for Classification - Multiclass via Logistic
14:19
11.4 Linear Models for Classification - Multiclass via Binary
11:36
12.1 Nonlinear Transformation - Quadratic Hypotheses
23:21
12.2 Nonlinear Transformation - Nonlinear Transform
09:53
12.3 Nonlinear Transformation - Price of Nonlinear Transform
15:38
12.4 Nonlinear Transformation - Structured Hypothesis Sets
09:37
13.1 Hazard of Overfitting - What is Overfitting
10:19
13.2 Hazard of Overfitting - The Role of Noise and Data Size
13:36
13.3 Hazard of Overfitting - Deterministic Noise
14:08
13.4 Hazard of Overfitting - Dealing with Overfitting
10:50
14.1 Regularization - Regularized Hypothesis Set
18:45
14.2 Regularization - Weight Decay Regularization
24:09
14.3 Regularization - Regularization and VC Theory
08:16
14.4 Regularization - General Regularizers
13:29
15.1 Validation - Model Selection Problem
15:34
15.2 Validation - Validation
13:25
15.3 Validation - Leave-One-Out Cross Validation
16:07
15.4 Validation - V-Fold Cross Validation
10:42
16.1 Three Learning Principles - Occam's Razor
09:37
16.2 Three Learning Principles - Sampling Bias
11:51
16.3 Three Learning Principles - Data Snooping
12:29
16.4 Three Learning Principles - Power of Three
08:50

（附课件代码）林轩田公认最好的【机器学习基石+技法】教程，全130集！
吴恩达LLM
2.5万 341

统计机器学习-张志华
该账户已被锁定
9.1万 803

李宏毅2020机器学习深度学习(完整版)国语
大佬的迷弟的粉丝
235.9万 4.5万

北邮《Python编程与实践》课程 (2020)
爱可可-爱生活
19万 3883

【2025】机器学习周志华亲讲-西瓜书全网最详讲解：附：书籍pdf+ppt
IT-小恒
25万 385

周志华老师亲讲-西瓜书全网最详尽讲解-1080p高清原版《机器学习初步》
yxl-bili
109.3万 4069

【白板推导】机器学习纯手工教学实战课程（135集全）机器学习算法数学基础、线性回归、降维、支持向量机SVM、概率图模型、EM算法、隐马、卡尔曼滤波一口气学完
人工智能基础
4.4万 44

如果大数定律失效，机器学习还能学吗？幂律分布可以告诉你答案
王木头学科学
6.1万 343

(强推)浙江大学-机器学习
啥都会一点的研究生
99.7万 7573

(强推)李宏毅2021/2022春机器学习课程
啥都会一点的研究生
299.3万 4.9万

AI智能体赋能课堂教学——技术很简单，难的是想法
爱可可-爱生活
9.3万 54

【已授权】李宏毅2025《机器学习》
啥都会一点的研究生
16.2万 1951

李宏毅机器学习(2017)
爱可可-爱生活
94.2万 2.4万

【斯坦福大学】CS229 机器学习 · 2018年（完结·中英字幕·机翻）
鬼谷良师
29.5万 2671

1.1 课程介绍【斯坦福21秋季：实用机器学习中文版】
跟李沐学AI
56万 1001

《机器学习》3小时期末速成课！期末速成丨考前突击丨期末不挂科丨考点总结
数学建模老哥
71.4万 1553

神经网络与深度学习（第1-6讲）
邱锡鹏nlp
49.6万 3836

机器学习基石，完整版 - 林轩田 - 台湾大学
林子木001
2.3万 37

【机器学习】林轩田200集基石+进阶台湾大学公开课
求知若渴谦虚若愚
1.1万 13

(新版中英!) B站公认最好的【吴恩达机器学习】教程！附课件代码 Machine Learning Specialization
吴恩达机器学习
96.6万 2470
展开
寰游赶海乐子看不停
客服
顶部
赛事库 课堂 2021拜年纪