0.25
0.5
0.75
1.25
1.5
1.75
2
Introduction to Machine Learning
Published on Oct 11, 201817986 Views
Related categories
Chapter list
Introduction to Machine Learning00:00
Outline00:39
Kinds of Machine Learning02:31
Supervised Learning03:11
Examples of Supervised Learning05:56
Reinforcement Learning07:22
Examples of Reinforcement Learning09:35
Unsupervised Learning11:14
Clustering - 112:41
Clustering - 213:25
Examples of Unsupervised Learning14:41
What Does a Data Look Like?16:54
Back to Supervised Learning18:35
What function do we use?19:32
Which Line?20:43
Fitting a Line21:21
Residuals - 122:57
Residuals - 223:49
Loss – How good or bad is our line?24:49
Least Squares26:59
So what are the weights?28:47
How do we know a line is right?29:11
Maximum Likelihood31:52
Regularization34:47
L1 and L2 regularization38:26
Bayesian Methods41:37
Marginal Likelihoods46:32
Bayesian Occam’s Razor50:43
Nonparametric Bayesian Models53:17
More Sophisticated Regression Models55:31
Example: Donner Party57:32
Donner Party Data - 158:39
Donner Party Data - 259:26
Moving on from Linear Regression01:00:13
Generalized Linear Models01:01:21
Logistic Regression01:02:25
The Logit01:04:21
The Logistic Regression Model01:05:44
Logistic Regression Plot01:06:49
Summary01:08:00
Moving Forward - 101:09:54
Moving Forward - 201:12:06
But Still..01:13:26
Missed Opportunities01:15:03