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从数据科学应用(R programming) 角度来学习统计与概率 intro to Data science

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转载自 edx and HarvardX https://rafalab.github.io/dsbook/ Intro to Data science with R 从应用角度入门概率与统计
公开课
概率
统计
statistics
harvard
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视频选集
（48/87）
01 intro
03:46
02 discrete probability
02:36
03 Monte carlo simulation with R
04:28
04 probability distribution
01:10
05 independence
06:33
06 combination and permutation
08:16
07 birthday problem
02:24
08 sapply
05:15
09 How many Monte Carlo experiments are enough
02:07
10 addition rule
02:11
11 monty hall problem
04:34
12 continuous probability
02:57
13 Theoretical Distribution
05:18
14 Probability Density
02:33
15 Monte Carlo Simulations
01:56
16 Other Continuous Distributions
01:41
17 Random Variables
01:26
18 Sampling Models
07:45
19 Distributions versus Probability Distributions
02:27
20 Notation for Random Variables
01:32
21 Central Limit Theorem
07:32
22 Averages and Proportions
03:27
23 Law of Large Numbers
01:53
24 How Large is Large in CLT?
01:30
25 The Big Short Interest Rates Explained
07:24
26 The Big Short
05:37
27 inference intro
03:34
28 Sampling Model Parameters and Estimates
04:25
29 The Sample Average
03:28
30 Polling versus Forecasting
00:55
31 Properties of Our Estimate
03:09
32 The Central Limit Theorem in Practice
04:54
33 Margin of Error
02:33
34 A Monte Carlo Simulation for the CLT
02:41
35 The Spread
01:35
36 Bias Why Not Run a Very Large Poll
02:04
37 Confidence Intervals
05:22
38 A Monte Carlo Simulation for Confidence Intervals
01:43
39 The Correct Language
00:49
40 Power
01:29
41 P Values
03:45
42 Poll Aggregators
05:22
43 Pollsters and Multilevel Models
02:22
44 Poll Data and Pollster Bias
04:42
45 Data Driven Models
04:14
46 Bayesian Statistics
01:19
47 Bayes Theorem
05:49
48 Bayes in Practice
03:28
49 The Hierarchical Model
10:04
50 Election Forecasting
04:44
51 Mathematical Representations of Models
05:52
52 Predicting the Electoral College
05:46
53 Forecasting
02:53
54 t distribution
03:49
55 Association Tests
04:17
56 Chi Squared Tests
05:27
57 regression intro
01:55
58 Moneyball
02:27
59 baseball basics
05:24
60 Bases on Balls or Stolen Bases
02:30
61 Correlation
02:35
62 Correlation Coefficient
03:22
63 Sample Correlation is a Random Variable
02:08
64 Anscombe Quartet Stratification
07:18
65 Bivariate Normal Distribution
03:30
66 Variance Explained
01:06
67 There are Two Regression Lines
01:29
68 Confounding Are BBs More Predictive
02:17
69 Stratification and Multivariate Regression
04:00
70 Linear Models
05:36
71 Least Squares Estimates (LSE)
02:27
72 The lm Function
01:21
73 LSE are Random Variables
03:26
74 Advanced dplyr Tibbles
03:04
74 Predicted Variables are Random Variables
01:46
75 Tibbles Differences from Data Frames
03:30
76 do
03:23
77 broom
02:21
78 Building a Better Offensive Metric for Baseball
08:26
79 On Base Plus Slugging
00:50
80 Regression Fallacy
05:32
81 Measurement Error Models
04:45
82 Correlation is Not Causation
04:38
83 Outliers
02:04
84 Reversing Cause and Effect
02:09
85 Confounders
04:48
86 Simpson Paradox
01:14

阿姆斯特丹大学统计课基础版Basic Statistics by University of Amsterdam and Coursera
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1.2万 124

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1.3万 12

哈佛通识概率普及课（特别推荐）Fat Chance: Probability from the Ground Up by Harvard and Edx
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1.2万 28

人人必备的数学思维Mathematical thinking by Stanford University and Coursera
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2.9万 45

哈佛大学最受欢迎的统计课Harvard Statistics 110 introduction
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1.6万 1

图解CNN论文：尝试用最少的数学读懂深度学习论文
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献给所有认为数学和深度学习很重要，但认为自己“不擅长数学”的人！
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2.3万 20

图解教材:概率机器学习（Murphy)
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1.3万 2

Economics of Money and Banking (coursera)
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1.9万 48

Santa Fe Institute 复杂系统公开课Complexity: An introduction
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2.0万 34

概率机器学习基础：MIT概率课图解笔记
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吴恩达第五课RNN笔记
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3.2万 127

Mathematics for Machine Learning (Coursera, Imperial college of London 完整版)
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4.4万 103

探索一句话版的机器学习与深度学习
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吴恩达机器学习图解笔记
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9.8万 285

图解机器学习的数学直觉：线性代数，微积分，PCA（全完结）
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图解教材: 模式识别与机器学习（Bishop)
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2.0万 12

fuzzySearch_bgar
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223 0

006_7ec8c787
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47 0

mismanagement_genius_hypothesis
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264 0
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