Foundation of Machine Learning

1. Foundation to Machine Learning
Waziri Shebogholo
University of Dodoma
April 10, 2019
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2. Truth
Machines don’t learn.
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3. Machine Learning
Subfield of computer science that is concerned with
building algorithms, which rely on the collection of
examples of the world problems to become useful.
Waziri Shebogholo (University of Dodoma) Foundation to Machine Learning April 10, 2019 3 / 20

4. Applications
Text classification
Natural Language Processing
Computer vision tasks, eg. Image recognition, face recognition
Medical diagnosis
Recommendation systems
Games eg. AlphaGo
Speech recognition eg. Siri, Google Home
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5. Types of Machine Learning
Machine Learning problems can be:-
1 Supervised Learning
2 Unsupervised Learning
3 Reinforcement Learning
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6. Supervised Learning
Terminologies
Example: an object or instance of data used
Dataset: collection of examples
Features: set of attributes, often represented as vector (feature
vector), associated with an object
Labels/Target
1 In classification, category associated with an example
2 In regression, real-valued numbers
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7. Supervised Learning
Goal
The goal of Supervised Learning algorithm is to use the dataset to create a
model that takes feature vector as input and output information that
allows to deduce a label for that feature vector.
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8. Unsupervised Learning
Unlabeled examples
Goal
The goal of Unsupervised Learning is to create a model that take a feature
vector and either transform it into another vector or a value that can be
used to solve a problem.
Clustering, model return cluster id
Dimensionalty reduction, model return a feature vector that have
fewer features
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9. Reinforcement Learning
Think of a machine in a certain environment that is able to perceive the
state of that environment as vector of features. The machine can execute
actions in every state. Different actions brings different rewards and could
also move the machine from one state to another.
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10. Goal
The goal of Reinforcement Learning algorithm is to learn a policy.
Policy is a function that takes the feature vector of a state and output an
optimal action to execute in that state.
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11. How it works?
Example
Let’s consider a problem in which we want to predict whether a person has
cancer or not.
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12. How it works?
1 Let’s frame this as supervised learning problem in which we first start
with gathering data.
2 Data for supervised learning problems is a collection of pairs
inputs, outputs
After having data, we need to choose a Learning algorithm .
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13. How it works?
Model
y = wx + b
Goal
The goal of Learning algorithm is to leverage the dataset and find optimal
values of parameters w and b.
How?
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14. How it works?
Optimization
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15. What you’ll hear the most with regard to a model?
1 Parameters
2 Hyperparameters
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16. What you’ll hear the most with regard to a model?
Parameters are variables that define the model learned by the learning
algorithm. They are modified by the learning algorithm based on the
training data. We train models to find these variables.
Hyperparameter is a property of learning algorithm mostly, having a
numerical value. They influence the way the algorithm works.
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17. Fundamental Algorithms
1 Linear regression
2 Logistic regression
3 Decision tree
4 Random Forest *
5 Support Vector Machine
6 k-Nearest Neighbors
7 Neural Networks
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18. Linear Regression
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19. Best practices
Feature Engineering
Learning algorithm selection
Data splitting
Underfitting and overfitting
Regularization
Model performance assesment
Hyperparameter tuning
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20. Thank you.
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