Machine learning allows computers to learn without being explicitly programmed. Neural networks are computing systems inspired by the brain that consist of neurons and connections between them. There are different types of machine learning including supervised learning for classification and regression, unsupervised learning like clustering, and transfer learning. Frameworks like Core ML can take models created in other frameworks and deploy them on iOS for tasks like object detection from images using bounding boxes and annotations.

1. Machine learning

3. Machine learning gives computers the ability to
learn without being explicitly programmed.

4. Neural networks
Computing systems inspired (loosely)
by the brain

5. Neuron - unit of computation
ŷ
x1
x2
x3
x4
x - real number
non-linear function
of the sum of x

6. Neuron network
ŷ
Input layer Hidden layer Output layer

7. Training (deep learning)
ŷ
Input layer Hidden layer Output layer
Loss function
Loss function adjusts connections weights as learning proceeds

8. Transfer learning
ClassifierBottleneck
Retrain

9. Supervised learning
A
B
C
Classification Regression

10. Clustering
A
B
D
C
Unsupervised learning

11. Frameworks

12. Manchester United Not Manchester United

13. Model creation

14. Bounding boxes
Origin 575, 320
width 410
height 450

15. Turi Create
data = tc.image_analysis.load_images('train', with_path=True)
data = data.sort(['path'])
data['annotations'] = [
[{'coordinates': {'height': 450, 'width': 410, 'x': 575, 'y': 320}, 'label': 'manch'}], #0
[{'coordinates': {'height': 271, 'width': 261, 'x': 280, 'y': 176}, 'label': 'manch'}], #1
[{'coordinates': {'height': 177, 'width': 177, 'x': 254, 'y': 250}, 'label': 'manch'}], #2
…
[{'coordinates': {'height': 400, 'width': 380, 'x': 360, 'y': 235}, 'label': 'manch'}], #31
[{'coordinates': {'height': 190, 'width': 190, 'x': 250, 'y': 205}, 'label': 'manch'}], #32
[{'coordinates': {'height': 170, 'width': 170, 'x': 350, 'y': 620}, 'label': 'manch'}] #33
]
model = tc.object_detector.create(data)
model.export_coreml('manchester.mlmodel')
import turicreate as tc

16. Core ML framework

17. Core ML request
let request = VNCoreMLRequest(model: model, completionHandler: { [weak self] request, error in
self!?.processClassifications(for: request, error: error)
})
private var requests = [VNCoreMLRequest]()
let mlmodel = Manchester().model
let model = try VNCoreMLModel(for: mlmodel)
request.imageCropAndScaleOption = .scaleFill
requests.append(request)

18. Core ML request handler
let handler = VNImageRequestHandler(cvPixelBuffer: pixelBuffer,
orientation: CGImagePropertyOrientation(rawValue: UInt32(ex))!,
options: requestOptions)
guard let pixelBuffer = CMSampleBufferGetImageBuffer(sampleBuffer) else { return }
handler.perform(requests)

20. •course.fast.ai
•coursera.org/learn/machine-learning with Andrew Ng
•Swift by Sundell 22: "A path for learning”
•lynda.com iOS App Development: Core ML
•github.com/blob8129/MUnotMU
Resources