machine learning important topics, Summaries of Machine Learning

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Introduction to Machine

Learning

Introduction to Machine Learning

Define machine learning Objective Objective Illustrate key elements of machine learning

Objective

An Illustrative Example

|Given some example pictures, how a computer

can learn to differentiate dogs from cats?

Data Representation – Feature Extraction

|Raw data: Images |Features

Preprocessing for Feature Extraction

|Segmentation

|Filtering

|Various transformations

 All intended for facilitating feature extraction

|Good features should be invariant in some

sense.

Mathematical Models for Classification

Basic Machine Learning Paradigms

|Supervised learning:

• the training samples have labels.

|Unsupervised learning:

• the training set is not labeled.

|Reinforcement learning:

• Learning to take actions to maximize some notion of reward.

A Few Examples of Machine Learning (1/3)

|Learning to assess

skills in simulation- based laparoscopic surgery training

|Learning to predict best

answers in community Q & A

A Few Examples of Machine Learning (2/3)

|Tag prediction/recommendation

|Diabetic retinopathy detection

Broad Applications of Machine Learning

|Computer vision

|Speech recognition; natural language

processing

|Medical informatics

|Robotics

|Computational biology

|Information technology

|Finance

Information Technology

|Spam detection

|Web image search

|Recommendation

|Information filtering

|Community detection

|Adaptive advertisement

|Sentiment analysis