Overview of machine learning techniques

3. 4 Key techniques

4. Supervised Learning
● Data Driven
● Key Algorithms include
– Regression
– Naive Bayes
– Support Vector Machines (SVM)
– Classification and Regression Trees

5. Decision Tree

6. Unsupervised Learning
● Clustering.
– k-means.
– hierarchical clustering
● Anomaly detection.
● Approaches for learning latent variable
models such as.
– Expectation–maximization algorithm (EM)
– Method of moments.

7. K-Means Clustering
● k-means clustering aims to partition
observations into k clusters
● each observation belongs to the cluster with the
nearest mean

8. Reinforcement Learning

9. Reinforcement Learning
● An Reinforcement Learning agent learns by
receiving a reward or reinforcement from its
environment.
● No form of supervision other than its own
decision making policy.

10. Deep learning

11. Why Machine Learning Now?
Greater Processing Power ( CPU + GPU )
Adequate Storage
Distributed Computing Power
● Cloud based
● Local Network
Big Data

12. Open Source Software
● Stanford University NLP library
● H2O
● Scikit-learn
● Tensorflow ( Google )

13. Summary
● Improved computer power
● Tested algorithms
● Big data
● Cloud computing

14. ● Machine Learning
● Software Engineers
● Science Reseach
●
● Based London

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