1. WHAT IS MACHINE
LEARNING
Bhaskara Reddy Sannapureddy, Senior Project Manager @Infosys, +91-7702577769
2. WHAT IS MACHINE
LEARNING?
Automating automation
Getting computers to program themselves
Writing software is the bottleneck
Let the data do the work instead!
3. TRADITIONAL PROGRAMMING VS
Computer
Data
Program
Output
Computer
Data
Output
Program
MACHINE LEARNING
Traditional Programming
Machine Learning
4. MAGIC?
No, more like gardening
Seeds = Algorithms
Nutrients = Data
Gardener = You
Plants = Programs
6. ML IN A NUTSHELL
Tens of thousands of machine learning algorithms
Hundreds new every year
Every machine learning algorithm has three components:
• Representation
• Evaluation
• Optimization
7. REPRESENTATION
Decision trees
Sets of rules / Logic programs
Instances
Graphical models (Bayes/Markov nets)
Neural networks
Support vector machines
Model ensembles
Etc.
8. EVALUATION
Accuracy
Precision and recall
Squared error
Likelihood
Posterior probability
Cost / Utility
Margin
Entropy
K-L divergence
Etc.
10. TYPES OF LEARNING
Supervised (inductive) learning
• Training data includes desired outputs
Unsupervised learning
• Training data does not include desired outputs
Semi-supervised learning
• Training data includes a few desired outputs
Reinforcement learning
• Rewards from sequence of actions
11. INDUCTIVE LEARNING
Given examples of a function (X, F(X))
Predict function F(X) for new examples X
• Discrete F(X): Classification
• Continuous F(X): Regression
• F(X) = Probability(X): Probability estimation
12. SUPERVISED AND
UNSUPERVISED LEARNING
Supervised learning
• Decision tree induction
• Rule induction
• Instance-based learning
• Bayesian learning
• Neural networks
• Support vector machines
• Model ensembles
• Learning theory
Unsupervised learning
• Clustering
• Dimensionality reduction
14. ML IN PRACTICE
Understanding domain, prior knowledge, and goals
Data integration, selection, cleaning,
pre-processing, etc.
Learning models
Interpreting results
Consolidating and deploying discovered knowledge
Loop
15. CLUSTERING STRATEGIES
K-means
• Iteratively re-assign points to the nearest cluster center
Agglomerative clustering
• Start with each point as its own cluster and iteratively merge the closest clusters
Mean-shift clustering
• Estimate modes of pdf
Spectral clustering
• Split the nodes in a graph based on assigned links with similarity weights
As we go down this chart, the clustering strategies have
more tendency to transitively group points even if they are
not nearby in feature space
16. THE MACHINE LEARNING
FRAMEWORK
Apply a prediction function to a feature representation of the
image to get the desired output:
Slide credit: L. Lazebnik
17. THE MACHINE LEARNING
FRAMEWORK
y = f(x)
output prediction
function
Image
feature
Training: given a training set of labeled examples {(x1,y1), …, (xN,yN)}, estimate the
prediction function f by minimizing the prediction error on the training set
Testing: apply f to a never before seen test example x and output the predicted value y = f(x)
Slide credit: L. Lazebnik