Probability and Statistics for Machine Learning

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00001 Probability and Statistics for Machine Learning - Introduction.mp4

03:21

00002 Topics.mp4

00:37

00003 1.1 Orientation to the Machine Learning Foundations Series.mp4

02:48

00004 1.2 What Probability Theory Is.mp4

07:04

00005 1.3 Events and Sample Spaces.mp4

04:56

00006 1.4 Multiple Observations.mp4

05:58

00007 1.5 Factorials and Combinatorics.mp4

04:49

00008 1.6 Exercises.mp4

08:52

00009 1.7 The Law of Large Numbers and the Gambler s Fallacy.mp4

11:17

00010 1.8 Probability Distributions in Statistics.mp4

05:23

00011 1.9 Bayesian versus Frequentist Statistics.mp4

08:43

00012 1.10 Applications of Probability to Machine Learning.mp4

08:47

00013 Topics.mp4

00:35

00014 2.1 Discrete and Continuous Variables.mp4

04:51

00015 2.2 Probability Mass Functions.mp4

05:56

00016 2.3 Probability Density Functions.mp4

04:09

00017 2.4 Exercises on Probability Functions.mp4

02:30

00018 2.5 Expected Value.mp4

06:21

00019 2.6 Exercises on Expected Value.mp4

04:26

00020 Topics.mp4

00:22

00021 3.1 The Mean a Measure of Central Tendency.mp4

07:19

00022 3.2 Medians.mp4

02:56

00023 3.3 Modes.mp4

06:22

00024 3.4 Quantiles - Percentiles Quartiles and Deciles.mp4

07:44

00025 3.5 Box-and-Whisker Plots.mp4

11:56

00026 3.6 Variance a Measure of Dispersion.mp4

07:45

00027 3.7 Standard Deviation.mp4

03:21

00028 3.8 Standard Error.mp4

04:53

00029 3.9 Covariance a Measure of Relatedness.mp4

12:43

00030 3.10. Correlation.mp4

07:45

00031 Topics.mp4

00:21

00032 4.1 Joint Probability Distribution.mp4

03:56

00033 4.2 Marginal Probability.mp4

05:11

00034 4.3 Conditional Probability.mp4

06:14

00035 4.4 Exercises.mp4

05:38

00036 4.5 Chain Rule of Probabilities.mp4

03:35

00037 4.6 Independent Random Variables.mp4

03:24

00038 4.7 Conditional Independence.mp4

05:01

00039 Topics.mp4

00:42

00040 5.1 Uniform.mp4

04:14

00041 5.2 Gaussian - Normal and Standard Normal.mp4

08:41

00042 5.3 The Central Limit Theorem.mp4

15:28

00043 5.4 Log-Normal.mp4

02:29

00044 5.5 Exponential and Laplace.mp4

04:45

00045 5.6 Binomial and Multinomial.mp4

08:06

00046 5.7 Poisson.mp4

03:36

00047 5.8 Mixture Distributions.mp4

03:29

00048 5.9 Preprocessing Data for Model Input.mp4

03:21

00049 5.10 Exercises.mp4

02:48

00050 Topics.mp4

00:28

00051 6.1 What Information Theory Is.mp4

01:11

00052 6.2 Self-Information Nats and Bits.mp4

06:20

00053 6.3 Shannon and Differential Entropy.mp4

07:10

00054 6.4 Kullback-Leibler Divergence and Cross-Entropy.mp4

05:29

00055 Topics.mp4

00:35

00056 7.1 Applications of Statistics to Machine Learning.mp4

09:23

00057 7.2 Review of Essential Probability Theory.mp4

12:08

00058 7.3 z-scores and Outliers.mp4

10:06

00059 7.4 Exercises on z-scores.mp4

03:58

00060 7.5 p-values.mp4

13:32

00061 7.6 Exercises on p-values.mp4

06:38

00062 Topics.mp4

00:35

00063 8.1 Single-Sample t-tests and Degrees of Freedom.mp4

10:36

00064 8.2 Independent t-tests.mp4

12:57

00065 8.3 Paired t-tests.mp4

13:30

00066 8.4 Applications to Machine Learning.mp4

04:13

00067 8.5 Exercises.mp4

07:55

00068 8.6 Confidence Intervals.mp4

10:27

00069 8.7 ANOVA - Analysis of Variance.mp4

03:09

00070 Topics.mp4

00:31

00071 9.1 The Pearson Correlation Coefficient.mp4

06:58

00072 9.2 R-squared Coefficient of Determination.mp4

02:55

00073 9.3 Correlation versus Causation.mp4

03:46

00074 9.4 Correcting for Multiple Comparisons.mp4

03:36

00075 Topics.mp4

00:45

00076 10.1 Independent versus Dependent Variables.mp4

05:23

00077 10.2 Linear Regression to Predict Continuous Values.mp4

07:16

00078 10.3 Fitting a Line to Points on a Cartesian Plane.mp4

15:45

00079 10.4 Linear Least Squares Exercise.mp4

05:09

00080 10.5 Ordinary Least Squares.mp4

13:09

00081 10.6 Categorical Dummy Features.mp4

17:45

00082 10.7 Logistic Regression to Predict Categories.mp4

13:23

00083 10.8 Open-Ended Exercises.mp4

03:58

00084 Topics.mp4

00:36

00085 11.1 Machine Learning versus Frequentist Statistics.mp4

07:03

00086 11.2 When to Use Bayesian Statistics.mp4

02:24

00087 11.3 Prior Probabilities.mp4

05:28

00088 11.4 Bayes Theorem.mp4

12:46

00089 11.5 Resources for Further Study of Probability and Statistics.mp4

02:03

00090 Probability and Statistics for Machine Learning - Summary.mp4

01:03

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