Introduction to Machine Learning

1. Machine Learning

2. Disclaimer
Images used in this presentation are collated from
different internet sources. The content is used
only for the sake of understanding the concepts of
Machine Learning. Exercise your own judgement
regarding the suitability of the content.
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5. Understanding Data Science, Machine Learning, Artificial Intelligence
and Deep Learning
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6. Understanding Data Science, Machine Learning, Artificial Intelligence
and Deep Learning
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7. When do we use Machine Learning?
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10. Classic Example of Machine Learning
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11. Modern Example of Machine Learning
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12. Modern Example of Machine Learning
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13. Modern Example of Machine Learning
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Case Study : Google Duplex
Google Assistant can make phone calls on behalf of
users.

16. Traditional Programming vs. Machine Learning
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17. What is Learning?
• Learning is any process by which a system improves performance
from experience- Herbert Simon
• Learning is not knowing what you do not know but
strengthening/enforcing what you already know.
• Learning is a repetitive process, process of refinement/
improvement.
• Learning is trying to understand (concept), recognize patterns,
trends, anomalies, summarizing, quantifying, qualifying a given
situation.
• Learning is through experience, through example, presented as
data.
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21. What is Machine Learning?
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Machine Learning is an application of AI that provides systems the ability
to automatically learn and improve from experience without being
explicitly programmed.
A computer program is said to learn from Experience E with
respect to some Task T and some Performance measure P, if
its performance on T, as measured by P, improves with
experience E.
Eg. Smart Homes
T: Estimate the desired Temperature
E: Learning from temperature dataset
P: Accuracy of the desired temperature

22. Defining the Learning Task
Improve on task T, with respect to Performance metric P, based on Experience E
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Sr. No.
TASK (T) PERFORMANCE (P) EXPERIENCE (E)
1 Playing Checkers % of games won against an
arbitrary opponent.
Playing practice games against itself.
2 Recognizing hand written words % of words correctly
classified.
Databased of human-labelled images of
handwritten words.
3 Driving on four-lane highways
using vision sensors
Average distance travelled
before a human-judged
error.
A sequence of images and steering
commands recorded while observing a
human driver.
4 Categorize email messages as
spam or legitimate
% of email messages
correctly classified.
Database of emails, some with human-
given labels.

23. How does Machine Learning Works?
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24. How does Machine Learning Works?
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25. How does Machine Learning Work?
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26. Machine Learning Phases
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Phase 1: Learning Phase 2: Prediction

27. Dimensionality Reduction
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The number of training examples required increases
exponentially with dimensionality.
• The classifier’s performance usually will degrade for a large number of
features!

28. Types of Data
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29. Discrete Data
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•You can count the data. It is usually units counted in whole
numbers.
•The values cannot be divided into smaller pieces and add
additional meaning.
•You cannot measure the data. By nature, discrete data cannot be
measured at all. For example, you can measure your weight with the
help of a scale. So, your weight is not a discrete data.
•It has a limited number of possible values e.g. days of the month.
•Discrete data is graphically displayed by a bar graph.

30. Examples of Discrete Data
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31. Continuous Data
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• In general, continuous variables are not counted.
• The values can be subdivided into smaller and smaller
pieces and they have additional meaning.
• The continuous data is measurable.
• It has an infinite number of possible values within an
interval.
• Continuous data is graphically displayed by histograms.

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34. Key Characteristic of Ordinal Data
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36. 3 Key Components of Machine Learning
Representation: how to represent knowledge ? (Select a
model to represent data)
Examples include decision trees, sets of rules, instances,
graphical models, neural networks, support vector
machines, model ensembles..
Evaluation: optimize the objective function, given
different values for the model parameters
Confusion matrix, accuracy, prediction and recall,
squared error, likelihood, posterior probability, cost,
margin, entropy
Optimization: estimate model parameters using
optimization methods-Greedy Search
Variables, parameters, or generalized loss/error
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37. Types of Learning
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38. Types of Learning
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39. Types of Machine Learning
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40. Types of Learning
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41. Supervised Learning
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42. UnSupervised Learning
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43. Reinforcement Learning
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Reinforcement Learning is learning what to do and how to map
situations to actions. The end result is to maximize the numerical
reward signal. The learner is not told which action to take, but
instead must discover which action will yield the maximum
reward.

44. Reinforcement Learning
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45. Errors in Machine Learning
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ERROR = noise2 + bias2 + variance
Unavoidable
error
Error due to
incorrect
assumptions
Error due to
variance of
training samples
High Bias leads to Underfitting

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High Variance leads to Overfitting

48. Overfitting & Underfitting
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49. Bias and Variance Trade-off
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50. Type I and Type II Error
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51. Metrics for Evaluation
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1. Accuracy
Accuracy =
Accuracy or the overall success rate =
True Positive Rate =
False Positive Rate =

52. Metrics for Evaluation
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2. Confusion Matrix
Test Set : 1100 images (1000 are Non-Cat and 100 are Cat images
TP: 90
FN: 10
TN: 940
FP: 60

53. Metrics for Evaluation
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3. Precision and Recall
Precision =
Recall or Sensitivity or True Positive Rate =
Specificity or True Negative Rate =

54. Metrics for Evaluation
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4. F1 Score
F1 Score= 2*
F1 Score is used to measure a test’s accuracy

55. Metrics for Evaluation
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56. Classification Measures: Error Rates
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57. Classification Accuracy: Estimating Error Rates
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 Partition: Training-and-testing
 use two independent data sets, e.g., training set (2/3), test set(1/3)
 used for data set with large number of samples
 Cross-validation
 divide the data set into k subsamples
 use k-1 subsamples as training data and one sub-sample as test
data --- k-fold cross-validation
 for data set with moderate size
 Bootstrapping (leave-one-out)
 for small size data

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K-Fold Cross-Validation

60. Classification Accuracy: Estimating Error Rates
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• There are several factors affecting the performance:
Types of training provided
The form and extent of any initial background knowledge
The type of feedback provided
The learning algorithms used
• Two important factors:
Modeling
Optimization

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