#ML (1) Definition

1.Machine Learning Definition

Machine Learning:

“Field of study that gives computers the ability to learn without being explicitly programmed.”
– Arthur Samuel (1959)

“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 M. Mitchell (1997)

2.ML Algorithms

– Supervised Learning

Definition: The computer is presented with example inputs and their desired outputs, given by a “teacher”, and the goal is to learn a general rule that maps inputs to outputs.

Supervised Learning learns from being given “right answers”.

eg 1.

Regression : dataset(right answer) –> to Fit a function
Logistic Regression (Classification) –> to Predict categories

eg 2.

>=2 inputs: to Find boundary that separates out different categories

– Unsupervised Learning

Definition: No labels are given to the learning algorithm, leaving it on its own to find structure in its input. Unsupervised learning can be a goal in itself (discovering hidden patterns in data) or a means towards an end (feature learning).

Unsupervised learning find something interesting in unlabeled data.

eg 1.

Clustering: to Find articles

eg 2.

Reinforcement Learning

Definition: A computer program interacts with a dynamic environment in which it must perform a certain goal (such as driving a vehicle or playing a game against an opponent). As it navigates its problem space, the program is provided feedback that’s analogous to rewards, which it tries to maximize.[5] Although each algorithm has advantages and limitations, no single algorithm works for all problems.[36][37][38]