Machine Learning. Lecture # 1. Contents. Why machine learning (ML) useful ? What is ML ? Key steps of learning Types of ML algorithms. Why Machine learning . Computational power is available (Resource) Recent progress in algorithms and theory (Resource)
Lecture # 1
What makes a 2?
T: Play checkers
P: % of games won
E: Playing against self
Academic records are rather diverse so we might limit the summaries to select few courses. For example, we summaries the ith student (say peter) with vector
Xi=[A C B]
Where grade may correspond to numeric values
We have to rely on the method of evaluating the accuracy of our predictions to select among the possible refinements
Major main types are:
•Unlike supervised learning which analyse class-labeled data objects, clustering analyse data objects without consulting a class. In fact class labels are not present in data because they are not known
• Major questions of the clustering are
-Are there any “groups” in the data ?
-What is each group ?
-How many ?
-How to identify them?
Euclidean Distance Based Clustering in 3-D space