Our Technology Lead Cory Zibell gave a presentation about Machine Learning. The algorithms, processes, techniques, and modules that it entails. It's meant for anyone to grasp, check it out!

1. M A C H I N E
L E A R N I N G

2. M A C H I N E
L E A R N I N G
Algorithms
Machine learning is a discipline focused
on getting a computer to analyze data
without explicit instructions, and come up
with conclusions about that data.

3. M A C H I N E
L E A R N I N G
Algorithms

4. M A C H I N E
L E A R N I N G
Algorithms
PROCESSES
Techniques
MODELS

5. M A C H I N E
L E A R N I N G
Algorithms
PROCESSES
Techniques
MODELS

6. M A C H I N E
L E A R N I N G
Algorithms
PROCESSES
Techniques
MODELS

7. M A C H I N E
L E A R N I N G
Algorithms
PROCESSES
Techniques
MODELS

8. M A C H I N E
L E A R N I N G
Algorithms

9. M A C H I N E
L E A R N I N G
Algorithms
An algorithm is a step by step
description on how to calculate
an output from an input

11. M A C H I N E
L E A R N I N G
Algorithms
y = f(x)

12. M A C H I N E
L E A R N I N G
Algorithms
y = 12-x
This is the algorithm

13. M A C H I N E
L E A R N I N G
Algorithms
y = 12-x
x = 6
y = 12-6
y = 6
input:
output:

14. M A C H I N E
L E A R N I N G
Algorithms
y = 12-x
x = 6
y = 12-6
y = 6
input:
output:

15. M A C H I N E
L E A R N I N G
Algorithms
y = x/2
x = 12
y = 6
let's try
algorithm

16. M A C H I N E
L E A R N I N G
Algorithms
y = 12-x
This is the original algorithm

17. M A C H I N E
L E A R N I N G
Algorithms
y = 24-x
x = 24
y = 24-6
y = 18
input:
output:

18. M A C H I N E
L E A R N I N G
Algorithms
y = x/2
x = 24
y = 6
let's try
algorithm

19. M A C H I N E
L E A R N I N G
Algorithms
y = x/2
x = 24
let's try
algorithm
y = 6

20. M A C H I N E
L E A R N I N G
Algorithms
let's try y = f(x)

21. M A C H I N E
L E A R N I N G
Algorithms
y = 12-x
This is the algorithm

22. M A C H I N E
L E A R N I N G
Algorithms
y = (6*4-6-6)-x

23. M A C H I N E
L E A R N I N G
Algorithms
x
y
input:
output:

24. M A C H I N E
L E A R N I N G
Algorithms
Supervised
Reinforcement
Unsupervised

25. M A C H I N E
L E A R N I N G
Algorithms
Supervised machine learning is
the most common. The goal is
to figure out the algorithm
between an input and output.

26. M A C H I N E
L E A R N I N G
Algorithms
Supervised machine learning
approaches two types of problems.

27. M A C H I N E
L E A R N I N G
Algorithms
Classification
Regression
| |
y = f(x)
Facial detection
Object recognition
Speech to text
Sentiment analysis
Spam filtering
Hardware failure
Health failure
Financial market shifts
Customer churn prediction

28. M A C H I N E
L E A R N I N G
Algorithms
Supervised
Reinforcement
Unsupervised

29. M A C H I N E
L E A R N I N G
Algorithms
Supervised Unsupervised
Boundary
Clusters

30. M A C H I N E
L E A R N I N G
Algorithms
The system has no y,
just many bits of x
(known output)
(known inputs)

31. M A C H I N E
L E A R N I N G
Algorithms

33. M A C H I N E
L E A R N I N G
Algorithms
Unsupervised machine learning
takes arbitrary (unlabelled)
data and tries to find
trends and groups.

34. M A C H I N E
L E A R N I N G
Algorithms
This is commonly called
"clustering," e.g. finding
similarities in bits of data.
Clusters

35. M A C H I N E
L E A R N I N G
Algorithms
Inversely, it can also be
used to find anomalies.
Clusters

36. M A C H I N E
L E A R N I N G
Algorithms
Unsupervised machine learning
is far less common, but
represents the "future" of many
AI applications, since most data
in the world is "unlabelled."

37. M A C H I N E
L E A R N I N G
Algorithms
Unsupervised machine learning is
also used for
"Dimensionality Reduction,"
e.g. reducing the number of
columns in your data that aren't
unique.

38. M A C H I N E
L E A R N I N G
Algorithms
Supervised
Reinforcement
Unsupervised

39. M A C H I N E
L E A R N I N G
Algorithms
Reinforcement machine learning
uses a "reward system" to teach
a machine to make continuously
"rewarding decisions."

40. M A C H I N E
L E A R N I N G
Algorithms
interpreter
reward
agent
environment
state
action

41. M A C H I N E
L E A R N I N G
Algorithms
This is used in many things from
video games to self-driving cars.

42. M A C H I N E
L E A R N I N G
Algorithms
It's also similar to "recommender
systems," where a system tries to
find associated products, content,
etc that a user might like.

43. M A C H I N E
L E A R N I N G
Algorithms
Classification
Regression
Clustering
Dimensionality Reduction
Reinforcement Learning
Logistic Regression
Support Vector Machines (SVM)
Random Forest (RF)
Naive Bayes
Genetic Algorithms
Principle Component Analysis (PCA)
Linear Discriminant Analysis (LDA)
Autoencoders
Linear Regression
Polynomial Regression
Neural Networks
Regression Trees and Random Forests
K-Means
Linear Discriminant Analysis
Recommender Systems
K-Nearest Neighbor
Matrix Factorization
(Stochastic Gradient Descent,
Alternating Least Squares)
Association Rules (Apriori, Elcat)
Deep Neural Networks
Q-Learning
State-Action-Reward-State-Action (SARSA)
Deep Q Network (DQN)
Deep Deterministic Policy Gradient (DDPG)

44. M A C H I N E
L E A R N I N G
processes

45. M A C H I N E
L E A R N I N G
PROCESSES
Let's train a system to figure
out whether an alcohol is
🍷wine or 🍺 beer.

46. M A C H I N E
L E A R N I N G
All machine learning starts with
some form of "data."
PROCESSES

47. M A C H I N E
L E A R N I N G
🍺 🍷
Attribute 1: Color (as a wavelength of light)
Attribute 2: Alcohol by Volume (as a percentage)
PROCESSES

48. M A C H I N E
L E A R N I N G
Next, we go to the grocery store
and get beer and wine, to
gather data.
PROCESSES

49. M A C H I N E
L E A R N I N G
Color (nm) Alcohol % Beer or Wine?
610 5 Beer
599 13 Wine
693 14 Wine
PROCESSES

50. M A C H I N E
L E A R N I N G
We then get the data into format
& location suitable for machine
learning. This is called
data preparation.
PROCESSES

51. M A C H I N E
L E A R N I N G
1. Collect Data
2. Randomize Order
3. Visualize Data to look for
pre-existing patterns
4. Split data into "training" and
"performance testing" sets.
PROCESSES

52. M A C H I N E
L E A R N I N G
Next we choose a model. I'll talk
about this more later, for now,
let's use a simple one.
PROCESSES

53. M A C H I N E
L E A R N I N G
Then we move onto training.
(the bulk of the process)
PROCESSES

54. M A C H I N E
L E A R N I N G
0
5
10
15
20
550 575 600 625 650
PROCESSES

55. M A C H I N E
L E A R N I N G
y = m(x) + b
output slope input y-intercept
PROCESSES

56. M A C H I N E
L E A R N I N G
0
5
10
15
20
550 575 600 625 650
PROCESSES

57. M A C H I N E
L E A R N I N G
y = m(x) + b
output slope input y-intercept
Weight: Multiplied Value
Bias: Added to the end result
slope
y-intercept
PROCESSES

58. M A C H I N E
L E A R N I N G
We then tweak weights and
biases in the algorithm to be
more accurate.
PROCESSES

59. M A C H I N E
L E A R N I N G
training data
model prediction
test & update
weights & biases
PROCESSES

60. M A C H I N E
L E A R N I N G
Finally, we evaluate the results
and modify as needed, tuning
parameters where necessary
(like number of training loops).
PROCESSES

61. M A C H I N E
L E A R N I N G
Final result: a functional
machine learning model.
model prediction
Color: 660nm
ABV: 12% 🍷
PROCESSES

62. M A C H I N E
L E A R N I N G
technIques

63. M A C H I N E
L E A R N I N G
TECHNIQUES
Feature Learning
The ability of a system to automatically
detect classifications in raw data.

64. M A C H I N E
L E A R N I N G
Sparse Dictionary Learning
Learning a more generic
representation of input data that
gets rid of noise and outliers.
TECHNIQUES

65. M A C H I N E
L E A R N I N G
TECHNIQUES

66. M A C H I N E
L E A R N I N G
Anomaly Detection
Identification of rare items, events
or observations which raise
suspicions by differing significantly
from the majority of the data.
TECHNIQUES

67. M A C H I N E
L E A R N I N G
Decision Trees
Determining a likelihood particular
outcome based on a set of
observations.
TECHNIQUES

68. M A C H I N E
L E A R N I N G
Your chances of survival were good if you were
(i) a female or (ii) a male younger than 9.5
years with less than 2.5 siblings.
Titanic Survival Decision Tree TECHNIQUES

69. M A C H I N E
L E A R N I N G
Association Rules
Discovers interesting relations
between variables in large databases
TECHNIQUES

70. M A C H I N E
L E A R N I N G
For example, the
{onions, potatoes} => {burger}
rule found in the sales data of a
supermarket would indicate that if a
customer buys onions and potatoes together,
they are likely to also buy hamburger meat.
TECHNIQUES

71. M A C H I N E
L E A R N I N G
MODELS

72. M A C H I N E
L E A R N I N G
MODELS
Artificial Neural Networks
A framework for many
different machine learning
algorithms to work
together and process
complex data inputs.

73. M A C H I N E
L E A R N I N G
MODELS
Support Vector Machines
Finds a way to
accurately split
classes of data,
before it is
processed further.

74. M A C H I N E
L E A R N I N G
MODELS
Bayesian Networks
Known as "belief" or "causal"
networks. They predict outputs with
multiple inputs, taking into account
how inputs affect each other.

75. M A C H I N E
L E A R N I N G
MODELS
Bayesian Networks

76. M A C H I N E
L E A R N I N G
MODELS
Genetic Algorithms
Algorithms that mimic the process
of natural selection. Similar to
reinforcement learning, but rely
on more biologically inspired
things like genetic crossover,
mutation, and selection.

77. M A C H I N E
L E A R N I N G

78. M A C H I N E
L E A R N I N G
questions