gives us a great definition of machine learning:
“With traditional programming, you explicitly
tell a computer what it needs to do using code,
but with machine learning the computer finds
its own solution to a problem based on examples
you show it.” In practice, this means collecting
data or finding a pre-built dataset to train a
mathematical model, much like training a child to
recognize the difference between a dog and a cat
from various photos.
The trained model should make successful
predictions or classifications when presented
with unseen data, in a process called inference.
This process is similar to showing the child a new
photo of a cat and seeing if they guess correctly.
In practice, training a machine learning model
requires more complex math and computing
power than inference does. As a result, we often
see training occur on large desktops or servers,
which gives us the option of performing inference
on small embedded devices using the newly
trained model. Training on a microcontroller is
theoretically possible, but most microcontrollers
don’t have the memory and computing power
necessary to perform the required calculations.
From this, we can conclude that machine
learning is a subset of AI. All machine learning
achieves some goal, so it is a part of AI, but not
all AI programs are machine learning. Another
DEEPER LEARNING: Going Further With TinyML
common term you might run across is deep
learning, which was coined by Rina Dechter in
her 1986 research paper on machine learning
algorithms. Deep learning is the use of more
complex machine learning models to achieve
better accuracy. Therefore, deep learning is a
subset of machine learning (Figure
A
).
COLLECTING DATASETS
One of the biggest hurdles in machine learning
is gathering data for training process. For typical
ML training, called supervised learning, people
must carefully curate the dataset, which includes
labeling every sample by hand. Additionally, data
scientists must eliminate or limit any biases
in the dataset. For example, I created a voice-
activated Halloween pumpkin that would laugh
and flash whenever someone said “trick or treat”
(Figure
B
). I mistakenly used only one adult male
and one adult female voice to train the model to
recognize the phrase. As a result, the model was
biased toward adult voices; it was incapable of
correctly classifying children’s voices!
Bias, with regard to statistics and machine
learning, is some error or distortion that stems
from statistical analysis or model training. This
personal story illustrates a type of selection bias,
where the selection of data is not representative
of the population intended to be analyzed or used
24 makercampus.com
A
DEEP LEARNING
More complex models
MACHINE LEARNING
Learning from
past experiences
ARTIFICIAL
INTELLIGENCE
Achieves goal(s)
M77_022-31_SS_MLdeepDive_F1.indd 24M77_022-31_SS_MLdeepDive_F1.indd 24 4/11/21 12:58 PM4/11/21 12:58 PM
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