Machine Learning

Ms.A.S.Pathan, Assistant Professor, aspathan@coe.sveri.ac.in,

Department of Computer Science and Engineering

SVERI’s COE, Pandharpur

The term machine learning was coined in 1959 by Arthur Samuel, an American IBMerand pioneer in the field of computer gamingand artificial intelligence. A representative book of the machine learning research during the 1960s was the Nilsson's book on Learning Machines, dealing mostly with machine learning for pattern classification. Interest related to pattern recognition continued into the 1970s, as described by Duda and Hart in 1973.In 1981 a report was given on using teaching strategies so that a neural network learns to recognize 40 characters (26 letters, 10 digits, and 4 special symbols) from a computer terminal.

Tom M. Mitchell provided a widely quoted, more formal definition of the algorithms studied in the machine learning field: "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."This definition of the tasks in which machine learning is concerned offers a fundamentally operational definition rather than defining the field in cognitive terms. This follows Alan Turing's proposal in his paper "Computing Machinery and Intelligence", in which the question "Can machines think?" is replaced with the question "Can machines do what we (as thinking entities) can do?".

Machine learning (ML) is the study of computer algorithms that improve automatically through experience. It is seen as a subset of artificialintelligence. Machine learning algorithms build a mathematical model based on sample data, known as "training data", in order to make predictions or decisions without being explicitly programmed to do so. Machine learning algorithms are used in a wide variety of applications, such as email filtering and computer vision, where it is difficult or infeasible to develop conventional algorithms to perform the needed tasks.

Machine learning is closely related to computational statistics, which focuses on making predictions using computers. The study of mathematical optimization delivers methods, theory and application domains to the field of machine learning.Data miningis a related field of study, focusing on exploratory data analysisthrough unsupervised learning. In its application across business problems, machine learning is also referred to as predictive analytics.

Machine learning approaches

Early classifications for machine learning approaches sometimes divided them into three broad categories, depending on the nature of the "signal" or "feedback" available to the learning system.

These were:
Supervised learning: 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 mapsinputs to outputs.

Unsupervised learning: 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).

Reinforcement learning: 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 maximise.

Other approaches or processes have since developed that don't fit neatly into this three-fold categorization, and sometimes more than one is used by the same machine learning system. For example topic modeling, dimensionality reduction or meta learning^. As of 2020, deep learninghas become the dominant approach for much ongoing work in the field of machine learning .

Fig. Machine Learning

Applications:

Applications of machine learning are as follows

1. Virtual Personal Assistants

2. Predictions while Commuting

3. Videos Surveillance

4. Social Media Services

5.Email Spam and Malware Filtering

6.Online Customer Support

7. Search Engine Result Refining

8. Product Recommendations

9. Online Fraud Detection

 

Challenges:

Major Challenges of Deep Learning are as follows

1. Memory networks.

2. Natural language processing (NLP)                              

3. Attention

4. Understand deep nets training

5. One-shot learning

6. Deep reinforcement learning to control robots

7. Semantic segmentation

8.  Video training data

9. Object detection

10. Democratizing AI

Reference:

[1] RinaDechter (1986). Learning while searching in constraint-satisfaction problems. University of California, Computer Science Department, Cognitive Systems Laboratory.

[2] Igor Aizenberg, Naum N. Aizenberg, Joos P.L. Vandewalle (2000). Multi-Valued and Universal Binary Neurons: Theory, Learning and Applications. Springer Science & Business Media.

[3] Co-evolving recurrent neurons learn deep memory POMDPs. Proc. GECCO, Washington, D. C., pp. 1795-1802, ACM Press, New York, NY, USA, 2005.

[4] Bengio, Y.; Courville, A.; Vincent, P. (2013). "Representation Learning: A Review and New

Perspectives".IEEE Transactions on Pattern Analysis and Machine Intelligence. 35 (8): 1798–1828. arXiv:1206.5538. doi:10.1109/tpami.2013.50.

[5] LeCun, Yann; Bengio, Yoshua; Hinton, Geoffrey (28 May 2015). "Deep learning".Nature. 521 (7553): 436–444. Bibcode:2015Natur.521..436L. doi:10.1038/nature14539. PMID 26017442.

[6] Schulz, Hannes; Behnke, Sven (2012-11-01). "Deep Learning". KI - KünstlicheIntelligenz. 26 (4): 357–363.doi:10.1007/s13218-012-0198-z. ISSN 1610-1987

[7] Bengio, Yoshua; Lee, Dong-Hyun; Bornschein, Jorg; Mesnard, Thomas; Lin, Zhouhan (2015-02-13). "Towards Biologically Plausible Deep Learning".arXiv:1502.04156.

[8] Abdel-Hamid, O.; et al. (2014). "Convolutional Neural Networks for Speech Recognition".

IEEE/ACM Transactions on Audio, Speech, and Language Processing. 22 (10): 1533–1545.

doi:10.1109/taslp.2014.2339736.