Intro to Machine Learning and AI

Introduction to
Machine Learning and
AI
Matt Hamilton
Developer Advocate
SW Mobile
February 25, 2020

What is the difference
between Machine
Learning (ML) and
Artificial Intelligence
(AI)?
IBM Developer Advocate / Intro to Machine Learning and AI / February 25th, 2020 / © 2020 IBM Corporation 2

In short…
- If you are reading a Powerpoint: it is AI
- If you are reading Python: it is ML
[ Yes that is a flippant answer. Artificial Intelligence is a much broader
field, of which ML is just one aspect/implementation. But for now this is
a useful simplification ]

What is Machine
Learning?
Just a function that takes input and gives outputs, but the bit in the middle is ‘learned’ rather than explicitly coded. E.g.

Types of
machine
learning
• Supervised learning
• Unsupervised learning
• Reinforcement learning
• Generative Adversarial Networks

Supervised
Learning
We train the algorithm with
“known” data then ask it to
predict unknown.
Classification
e.g. “Here is 100 pictures of cats, and
100 pictures of dogs, predict whether
this new image is a cat or a dog”
Regression
e.g. “Here is historical temperature data
for Bristol, predict what the
temperature will be tomorrow”

Unsupervised
learning
We have no ‘labels’ for the
data ahead of time
Clustering
e.g. “Find all towns on this map of
mobile phone towers”
Association
e.g. “What other alerts do we get when
we get a disk-full alert?”

Reinforcement
Learning
“Learning by playing or doing”
Rules are not defined at the start, but a
reward is given based on behaviour

Generative
Adversarial
Networks
Generating new data (images or text)
Two neural networks try to out-do each
other

Types of machine
learning algorithms
There are many types of machine
learning algorithms, eg:
• Naive Bayes Classifier
• Support Vector Machines
• Random Forest
• Neural Networks

Simple Neural Network
example (Keras)
# compile the model
model.compile(optimizer='adam', loss='mse')
# train the model on first 8 values of data
model.fit(X[:8], y[:8], epochs=7000,
batch_size=32, verbose=0)
# predict the last two (unseen) values
preds = model.predict(X[8:])
print(preds)
import numpy as np
import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense
# define our data
X = np.array([0, 1, 2, 3, 4, 5, 6, 8, 9, 10])
y = np.array([0, 2, 4, 6, 8, 10, 12, 16, 18, 20])
# define out neural network model
model = Sequential()
model.add(Dense(5, input_shape=(1,))) # 5 hidden
neurons
model.add(Dense(1)) # 1 output neuron

Common issues starting
with ML
How do I frame the problem? Is it
classification? Is it regression? What do
I actually want to know?
• “What is the percentage chance of
rain tomorrow?”
Regression
• “Is there a greater than 60% chance
of rain tomorrow?”
Classification

Pre-processing data
Dealing with missing data
Making data stationary

Pre-processing data
De-trend data

Pre-processing data
Unbalanced sets (classification)

Feature Engineering
Using domain knowledge to feed some more meaningful
data to the algorithm
This is where much of the work and skill is in effectively
utilising ML.
’Mlle’ => ‘Miss’
‘Mme’ => ‘Mrs’
'Lady', 'Countess','Capt', ‘Dr', 'Major', 'Rev',
‘Sir', 'Jonkheer', ‘Dona' => ‘special’
What do we know of the cabin numbering and layout?
- Cabin A14 => Deck “A” = First class, amidships

Feature Engineering
Formatting data (e.g. financial limit order book data) as ‘graphical’ then use CNN to categorise

Toolkits
• Swift for Tensorflow
• CoreML
• Tensorflow lite for Android
• Scikit-learn
• Keras (now part of Tensorflow)
• Tensorflow
• Torch (pyTorch)
• Caffe
• Theano

IBM tools
• Watson Machine
Learning
• Watson AutoAI
• Watson Studio

Thank you.
Matt Hamilton
Developer Advocate
Matthew.Hamilton1@ibm.com
@hammertoe

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