Introduction of machine learning, its structures, and techniques with examples.

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Machine Learning
Lecture#5
Program: BS(DS)-Fall 2019
Instructor: Konpal Darakshan

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• A branch of artificial intelligence, concerned with the
design and development of algorithms that allow
computers to evolve behaviors based on empirical data.
• As intelligence requires knowledge, it is necessary for the
computers to acquire knowledge.
What is machine learning?

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Learning system model
Input
Samples
Learning
Method
System
Training
Testing

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Training and testing
Training set
(observed)
Universal set
(unobserved)
Testing set
(unobserved)
Data acquisition Practical usage

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• Training is the process of making the system able to learn.
• Rule:
• Training set and testing set come from the same distribution
• Need to make some assumptions or bias
Training and testing

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• There are several factors affecting the
performance:
• Types of training provided
• The form and extent of any initial background
knowledge
• The type of feedback provided
• The learning algorithms used
• Two important factors:
• Modeling
• Optimization
Performance

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• The success of machine learning system also depends on
the algorithms.
• The algorithms control the search to find and build the
knowledge structures.
• The learning algorithms should extract useful information
from training examples.
• There are perhaps 14 types of learning; they are
Learning Algorithms
Learning Problems
1. Supervised Learning
2. Unsupervised Learning
3. Reinforcement Learning
Hybrid Learning Problems
4. Semi-Supervised Learning
5. Self-Supervised Learning
6. Multi-Instance Learning
Statistical Inference
7. Inductive Learning
8. Deductive Inference
9. Transductive Learning
Learning Techniques
10. Multi-Task Learning
11. Active Learning
12. Online Learning
13. Transfer Learning
14. Ensemble Learning

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• First, we will take a closer look at three main types of
learning problems in machine learning:
• Supervised learning
• Prediction
• Classification (discrete labels), Regression (real values)
• Unsupervised learning
• Clustering
• Probability distribution estimation
• Finding association (in features)
• Dimension reduction
• Reinforcement learning
• Decision making (robot, chess machine)
Learning Algorithms

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Supervised learning Unsupervised learning
Semi-supervised learning
Learning Algorithms

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• Supervised learning
• Supervised learning describes a class of problem that involves using a model to learn a
mapping between input examples and the target variable.
• Models are fit on training data comprised of inputs and outputs and used to make predictions on test
sets where only the inputs are provided and the outputs from the model are compared to the withheld
target variables and used to estimate the skill of the model.
Machine learning structure

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• Unsupervised learning
• Unsupervised learning describes a class of problems that involves using a model to
describe or extract relationships in data.
• Compared to supervised learning, unsupervised learning operates upon only the input
data without outputs or target variables. As such, unsupervised learning does not have a
teacher correcting the model, as in the case of supervised learning.
Machine learning structure

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Under-fitting VS. Over-fitting (fixed N)
What are we seeking?
error
model = hypothesis + loss
functions

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• Supervised learning categories and techniques
• Linear classifier (numerical functions)
• Parametric (Probabilistic functions)
• Naïve Bayes, Gaussian discriminant analysis (GDA), Hidden
Markov models (HMM), Probabilistic graphical models
• Non-parametric (Instance-based functions)
• K-nearest neighbors, Kernel regression, Kernel density estimation,
Local regression
• Non-metric (Symbolic functions)
• Classification and regression tree (CART), decision tree
• Aggregation
• Bagging (bootstrap + aggregation), Adaboost, Random forest
Learning techniques

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• Techniques:
• Perceptron
• Logistic regression
• Support vector machine (SVM)
• Ada-line
• Multi-layer perceptron (MLP)
Learning techniques
• Linear classifier

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Classification
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• Example: Credit
scoring
• Differentiating between
low-risk and high-risk
customers from their
income and savings
Discriminant: IF income > θ1 AND savings > θ2
THEN low-risk ELSE high-risk
Model

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Learning techniques
Using perceptron learning
algorithm(PLA)
Training Testing
Error rate:0.10 Error rate: 0.156

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Using logistic regression
Training Testing
Error rate: 0.11 Error rate: 0.145
Learning techniques

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• Support vector machine (SVM):
• Linear to nonlinear: Feature transform and kernel
function
• Non-linear case
Learning techniques

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• Example: Price of a
used car
• x : car attributes
y : price
y = g (x | θ )
g ( ) model,
θ parameters
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Prediction: Regression
y = wx+w0

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Supervised Learning: Uses
Example: decision trees tools that create rules
•Prediction of future Cases:Use the rule to
predict the output for future inputs.
•Knowledge extraction: The rule is easy to
understand.
•Compression: The rule is simpler than the data
it explains
•Outlier detection: Exceptions that are not
covered by the rule, e.g., fraud
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Unsupervised Learning
• Learning “what normally happens”
• No output
• Clustering: Grouping similar instances
• Other applications: Summarization, Association Analysis
• Example applications
• Customer segmentation in CRM
• Image compression: Color quantization
• Bioinformatics: Learning motifs
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• Unsupervised learning categories and techniques
• Clustering
• K-means clustering
• Spectral clustering
• Density Estimation
• Gaussian mixture model (GMM)
• Graphical models
• Dimensionality reduction
• Principal component analysis (PCA)
• Factor analysis
Learning techniques

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Reinforcement Learning
• Topics:
• Policies: what actions should an agent take in a
particular situation
• Utility estimation: how good is a state (used by
policy)
• No supervised output but delayed reward
• Credit assignment problem (what was responsible for
the outcome)
• Applications:
• Game playing
• Robot in a maze
• Multiple agents, partial observability, ...
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•Face detection
•Object detection and recognition
•Image segmentation
•Multimedia event detection
•Economical and commercial usage
Applications