Intro
Title Page
Copyright and Credits
Packt Upsell
Contributors
Table of Contents
Preface
Chapter 1: Getting Started with Machine Learning
What is AI?
The motivation behind ML
What is ML?
Applications of ML
Digital signal processing (DSP)
Computer vision
Natural language processing (NLP)
Other applications of ML
Using ML to build smarter iOS applications
Getting to know your data
Features
Types of features
Choosing a good set of features
Getting the dataset
Data preprocessing
Choosing a model
Types of ML algorithms
Supervised learning
Unsupervised learning. Reinforcement learningMathematical optimizationAΜ aΜ#x80
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how learning works
Mobile versus server-side ML
Understanding mobile platform limitations
Summary
Bibliography
Chapter 2: Classification aΜ#x80
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Decision Tree Learning
Machine learning toolbox
Prototyping the first machine learning app
Tools
Setting up a machine learning environment
IPython notebook crash course
Time to practice
Machine learning for extra-terrestrial life explorers
Loading the dataset
Exploratory data analysis
Data preprocessing
Converting categorical variables
Separating features from labels
One-hot encoding. Splitting the dataDecision trees everywhere
Training the decision tree classifier
Tree visualization
Making predictions
Evaluating accuracy
Tuning hyperparameters
Understanding model capacity trade-offs
How decision tree learning works
Building a tree automatically from data
Combinatorial entropy
Evaluating performance of the model with data
Precision, recall, and F1-score
K-fold cross-validation
Confusion matrix
Implementing first machine learning app in Swift
Introducing Core ML
Core ML features
Exporting the model for iOS
Ensemble learning random forest. Training the random forestRandom forest accuracy evaluation
Importing the Core ML model into an iOS project
Evaluating performance of the model on iOS
Calculating the confusion matrix
Decision tree learning pros and cons
Summary
Chapter 3: K-Nearest Neighbors Classifier
Calculating the distance
DTW
Implementing DTW in Swift
Using instance-based models for classification and clustering
People motion recognition using inertial sensors
Understanding the KNN algorithm
Implementing KNN in Swift
Recognizing human motion using KNN
Cold start problem
Balanced dataset. Choosing a good kReasoning in high-dimensional spaces
KNN pros
KNN cons
Improving our solution
Probabilistic interpretation
More data sources
Smarter time series chunking
Hardware acceleration
Trees to speed up the inference
Utilizing state transitions
Summary
Bibliography
Chapter 4: K-Means Clustering
Unsupervised learning
K-means clustering
Implementing k-means in Swift
Update step
Assignment step
Clustering objects on a map
Choosing the number of clusters
K-means clusteringAΜ aΜ#x80
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problems
K-means++
Image segmentation using k-means
Summary.