2. What is MC Learning
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Thesubfield of computer science that “gives computers the ability tolearn
without being explicitlyprogrammed”.
(Arthur Samuel,1959)
Acomputer program is said to learn from experience Ewith respect to someclass of tasks Tand
performance measure Pif its performance at tasks in T
,as measured byP
,improveswith
experienceE.”
(T
omMitchell, 1997)
Using data foranswering questions
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40. High Bias and Low Variance
(Low Flexibility)
Low Bias and High Variance
(Too Flexibility)
Low Bias and High Variance
(Balanced Flexibility)
41. Bias Error:
The bias is known as the difference between the prediction of the values by the ML model and the correct
value. Being high in biasing gives a large error in training as well as testing data.
Variance Error:
Variance is the amount that the estimate of the target function will change if different training data was
used.
50. Types of Supervised ML
Supervised
Unsupervised
Reinforcement
Output is a discrete variable (e.g.,
Defaulter and Non Defaulter
Spam and non spam
Purchaser Non Purchaser)
Classification
Regression
Output is continuous (e.g.,
price of house, temperature)
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55. Types of Machine Learning Problems
Supervised
Unsupervised
Reinforcement
Supervised
Is this a cat or a dog?
Are these emails spam or not?
Unsupervised
Predict the market value of houses, given the
square meters, number of rooms, neighborhood,
etc.
Reinforcement
Learn through examples of which we knowthe
desired output (what we want topredict).
56. Types of Machine LearningProblems
Unsupervised
Supervised
There is no desired output. Learn somethingabout
the data. Latent relationships.
I want to find anomalies in the credit cardusage
patterns of my customers.
Reinforcement
I have photos and want to put them in 20
groups.
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57. Types of Machine LearningProblems
Unsupervised
Supervised
Reinforcement
Useful for learning structure in the data(clustering),
hidden correlations, reduce dimensionality,etc.
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58. Environment gives feedback via a positiveor
negative reward signal.
Unsupervised
Reinforcement
Supervised An agent interacts with an environment andwatches
the result of the interaction.
Types of Machine LearningProblems
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60. Data Gathering
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Might depend on humanwork
• Manual labeling for supervised learning.
• Domain knowledge. Maybe evenexperts.
May come for free, or “sortof”
• E.g., Machine Translation.
The more the better: Some algorithms need large amounts of data to be
useful (e.g., neural networks).
The quantity and quality of data dictate the modelaccuracy
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61. Data Preprocessing
61
Is there anything wrong with thedata?
• Missing values
• Outliers
• Bad encoding (fortext)
• Wrongly-labeled examples
• Biased data
• Do I have many more samples of one class
than the rest?
Need to fix/remove data?
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62. FeatureEngineering
62
What is a feature?
Afeature is an individual measurable
property of a phenomenon being observed
Our inputs are represented by a setof features.
T
oclassify spam email, features couldbe:
• Number of words that have beench4ng3d
like this.
• Language of the email (0=English,
1=Spanish)
• Number of emojis
Buy ch34p drugs
from the ph4rm4cy
now :) :) :)
(2, 0, 3)
Feature
engineering
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63. FeatureEngineering
63
Extract more information from existing data, not adding “new” dataper-se
• Making itmore useful
• With good features, most algorithms can learnfaster
It can be an art
• Requires thought and knowledge of thedata
T
wo steps:
• Variable transformation (e.g.,dates into weekdays, normalizing)
• Feature creation (e.g., n-grams for texts, if word is capitalizedto
detect names, etc.)
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65. 65
THE MACHINE LEARNING FRAMEWORK
y = f(x)
● Training: given a training set of labeled examples {(x1,y1), …,
(xN,yN)}, estimate the prediction function f by minimizing the
prediction error on the training set
● Testing: apply f to a never before seen test example x and
output the predicted value y = f(x)
output prediction
function
Image
feature
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66. Goal of training: making the correct prediction as often as possible
• Incremental improvement:
• Use of metrics for evaluating performance and comparingsolutions
• Hyperparameter tuning: more an art than ascience
Algorithm Selection &Training
66
Predict Adjust
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67. Summary
67
• Machine Learning is intelligent use of data to answer questions
• Enabled by an exponential increase in computing power anddata
availability
• Three big types of problems: supervised, unsupervised,reinforcement
• 5 stepsto every machine learning solution:
1. Data Gathering
2. Data Preprocessing
3. Feature Engineering
4. Algorithm Selection &Training
5. Making Predictions
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68. Generalization
● How well does a learned model generalize from the data it
was trained on to a new test set?
Training set (labels known) Test set (labels
unknown)
69. Generalization
● Components of generalization error
○ Bias: how much the average model over all training sets differ from the true
model?
■ Error due to inaccurate assumptions/simplifications made by the model
■ Using very less features
○ Variance: how much models estimated from different training sets differ from
each other
● Underfitting: model is too “simple” to represent all the relevant class
characteristics
○ High bias and low variance
○ High training error and high test error
● Overfitting: model is too “complex” and fits irrelevant characteristics
(noise) in the data
○ Low bias and high variance
○ Low training error and high test error
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71.
72. Bias-Variance Trade-off
• Models with too few parameters are
inaccurate because of a large bias (not
enough flexibility).
• Bias can also come due to wrong
assumption.
• Lead to Train error
• Models with too many parameters are
inaccurate because of a large variance
(too much sensitivity to the sample).
• Lead to Test Error
