Machine learning is the ability of machines to learn from experience and improve their performance on tasks over time without being explicitly programmed. It involves the development of algorithms that allow computers to learn from large amounts of data. There are different types of machine learning including supervised learning, unsupervised learning, and semi-supervised learning. The history of machine learning began in the 1950s with research into neural networks, pattern recognition, and knowledge systems. Significant developments occurred in each subsequent decade, including decision trees, connectionism, reinforcement learning, and support vector machines. Machine learning continues to progress and find new applications in areas like data mining, language processing, and robotics.

1. Machine Learning
Introduction

2. Introduction
• Machine Learning is simply the ability of the machine to learn from
the previous experience or history and perform better at a given
task, as the future mimics the past.
• Machine Learning is considered as a subfield of Artificial
Intelligence and it is concerned with the development of techniques
and methods which enable the computer to learn.
• In simple terms, it is considered the science of development of
algorithms which enable the machine to learn and perform tasks
and activities.
• Machine learning overlaps with statistics in many ways.
• Over the period of time many techniques and methodologies were
developed for machine learning tasks.
• Learning is classified basically into supervised learning,
unsupervised learning and semi-supervised learning.

3. ML history

4. 1950's & 60's
• The History of machine learning dates back to the 1950's
during the AI and cognitive science day's.
• Realization of domain knowledge for intelligence and lead
to knowledge systems.
• Pattern recognition emerged as a new field.
• Neural networks, perceptron, learning in the limit theory.
• Neurophysiological:Rosenblatt's
perceptron,Biological:Simulated evolution,
Psychological:Symbol processing systems, Statistical:
Control and pattern recognition, Samuel's checkers
program
• Theoretical:Minsky and Papert's criticism of the perceptron

5. 1970's
• Symbolic concept induction,knowledge acquisition
systems, Quinlan’s ID3; Michalski’s AQ and soybean
diagnosis results, Scientific discovery with BACON,
mathematical discovery with AM.
• Winston's ARCH:Learned concept of a blocks-world
arch, Buchanan and Mitchell's Meta-Dendral: Learned
mass-spectrometry prediction rules,
Michalski'sAQ11:Learned soybean disease diagnosis
rules, Quinlan's ID3: Learned chess end-game rules,
Fikes, Hart and Nilsson's MACROPS:Learned macro-
operators in blocks-world planning,Lenat's
AM:Discovered interesting mathematical concepts.

6. 1980's
• Continued progress on decision-tree and rule learning.
• Explanation-based learning, speedup learning; utility problem,
analogy, resurgence of connectionism (PDP, ANN), PAC learning,
experimental evaluation.
• In 1980, First workshop on Machine Learning was at CMU attended
by 30 participants.
• Extended to domains of planning, diagnostics, design and control.
• Explosion of research directions.
• Some new directions included Learning theory,Symbolic learning
algorithms,Connectionist (neural network) learning
algorithms,Clustering and discovery,Explanation-based
learning,Knowledge-guided inductive learning,Analogical and case-
based reasoning,Genetic algorithms.

7. 1990's
• Data mining; adaptive software agents & Information
Retrieval; reinforcement learning; theory refinement;
inductive logic programming; voting, bagging,
boosting, and stacking; learning Bayesian networks.
• Emergence of support vector machines.
• Maturity of the field was observed.
• Some new directions included Statistical comparisons
of algorithms, Theoretical analyses of algorithms,
Successful applications, Multi-relational
learning,Ensemble and Kernel Methods.
• Is Machine learning = Data mining (?)

8. 2000 & Beyond
• Rise of SVM: Kernal Machines, Ensembles,and statistical
relational learning.
• Interactions between symbolic machine learning,
computational learning theory, neural networks, statistics,
pattern recognition.
• New applications for ML techniques: knowledge discovery
in databases, language processing, robot control,
combinatorial optimization.
• To improve accuracy by learning ensembles, Scaling up
supervised learning algorithms, Learning complex
stochastic models (Hierarchical Mixture of Experts, Hidden
Markov Model, Dynamic Probabilistic Network).

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