Lecture Notes 1 Flashcards
defined machine
learning as “the field of study that gives
computers the ability to learn without being
explicitly programmed.”
Arthur Samuel (1959),
▹ His ideas and approaches helped shape the early
understanding of machine learning.
Arthur Samuel (1959),
worked on developing a checkers-playing
program that could learn and improve over time.
Arthur Samuel (1959),
learning algorithms work with
labeled data.
Supervised Learning
3 key aspects from Mitchell’s defintion
Learning from experience:
Task oriented
Performance Improvement
Examples Illustrating Mitchell’
Spam Filter
Recommendation System
Self Driving Car
Unsupervised learning deals with unlabeled
data.
Unsupervised Learning
provided a more formal
definition of machine learning which states
that “A computer program is said to learn
from experience E with respect to some
class of tasks T and performance measure P,
if its performance at tasks in T, as measured
by P, improves with experience E.”
Tom Mitchell (1998),
▸ The idea is to teach the machine to learn
how to do something.
Supervised Learning
Types of Learning Algorithms
▸ Supervised Learning
▸ Unsupervised Learning
▸ Reinforcement Learning
what are the
T
E
P
LP
in mItchell
Task
Experience
Performance Measure
Learning Process
▸ The algorithm learns to map the input to the
output based on the data provided.
Supervised Learning
example scenarios of Supervised Learning
Predicting Loan Eligibility at a Bank
Predicting Housing Prices
Identifying Fake News on Social Media
Predicting Energy Consumption in Buildings
Common Tasks in Supervised Learning
▸ Classification
▸ Regression
Some algorithms in Supervised Learning
▹ Linear Regression
▹ Logistic Regression
▹ Decision Trees
▹ K-Nearest Neighbors
