Machine Learning NPTEL
Introduction

Tom Mitchell 1997 > said to learn from experience with respect to some class of tasks and a performance measure P, if [ the learner's] performance at tasks in the class, as measured by P, improves with experience

Requirements
 Class of tasks
 Performance Measure
 Experience

Paradigms of ML
 Supervised Learning
 Classification : Categorial Output
 Regression : Continuous Output
 Unsupervised Learning
 Clustering : cohesive grouping
 Association : frequent cooccurances

Reinforcement Learning
 Learning Control
Task Measure Classification error Regression error Clustering scatter/purity Associations support/confidence Reinforcement Learning cost/reward
 Supervised Learning

Supervised Learning : Applications
 Credit Card Fraud detection
 Sentiment Analysis
 Churn prediction
 Medical Diagnosis

Unsupervised learning :
 finding clusters  bias => shape of cluster boundary in vector space
 Association rule mining
 Mininig Transactions

Reinforcement Learning :
 Trial and error
 self learning
 feedback from environment
 control system
 Applications:
 Game Playing
 Autonomous agents
 Adaptive Control  quadrotor control
 Combinatorial optimization  VLSI placement
 Intelligent Tutoring System
Proabability Theory

Properties of Set operations:
 Commutativity
 Associativity
 Distributivity
 DeMorgan's Law

Two events A and B are disjoint (or mutually exclusive) if A intersection B is empty set

Sigma algebra is a collection (F) of subsets of sample space (O) such that
 phi belongs to F
 if A is in F then A^{C} is also in F
 If A_{i} for every i in N, then U^{inf}_{i=1} A_{i} in F
A set A that belongs to F is called F measureable set (event)

power set is always sigma algebra

The concept of sigma algebra is powerful in the case where sample space (O) is uncountable

Probability Measure and Probability Space

A probability measure P on (O,F) is a function P:F>[0,1] satisfying: a. P(phi) = 0, P(O) = 1 b. if A_{1}, A_{2} ... is a collection of pairwise disjoint members of F, then:
