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Title: machine learning


1
An Introduction to Machine Learning
2
Have you ever wondered?
  • How Google classifies your mails as spam/non-spam?

3
Have you ever wondered? How Google translates
to more than 100 languages?
4
Have you ever wondered?
  • How they are making Self Driving Cars?

5
Have you ever wondered?
  • How Siri, Cortana etc. give you correct replies?

6
Have you ever wondered?
  • How products are recommended on the shopping
    sites?

7
How are these all being possible?
  • The answer is because of Machine learning

8
Problem statement
  • Logic for the problems easily solvable by
    human beings cant be easily implemented because
  • Working of Human Brains is not known to us.
  • A generalized program covering many of the
    existing patterns in the problem domain can not
    be designed.
  • The program will become highly complicated and is
    likely to miss many possible cases if we try to
    implement the logic.
  • Many problems are difficult for humans to solve
    also because of their incapability in figuring
    out the patterns.
  • we expect machines to solve those
    problems

9
  • E.g
  • To compute the probability that a credit card
    transaction is fraudulent.

10
Machine learning approach
  • Instead of writing a program by hand for each
    specific task ,we collect lots of examples or
    experiences belonging to the problem.
  • A machine learning algorithm takes all these
    examples and produces a set of numbers or actions
    that will be able to do the job.
  • This set of numbers or actions is then applied on
    to the new examples to produce the correct
    outputs.

11
What is Machine Learning?
  • Definition Machine learning is a branch of
    artificial intelligence which aims to create
    intelligent systems which do human like jobs by
    learning from a lot of relevant data.

12
Definition by Tom Mitchell
  • A computer program is said to learn from
    experience E with respect to some class of tasks
    T and performance measures P, if its performance
    at tasks in T ,as measured by P, improves with
    experience E.

13
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14
Types of Machine Learning
  • Machine learning algorithm can be classified into
    3 types

15
1.Supervised learning
16
2.Unsupervised learning
17
3.Reinforcement learning
18
Machine Learning Applications
  • 1) Image recognition
  • 2) Speech recognition
  • 3) Medical diagnosis
  • 4) Agriculture, Physics
  • 5) Email management, Robotics
  • 6) Music
  • 7) Mathematics
  • 8) Natural Language Processing and many more.
  •  

19
  • Advantages
  • 1. Easily identifies trends and patterns.
  • 2. No human intervention needed (automation)
  • 3. Continuous Improvement
  • 4. Handling multi-dimensional and multi-variety
    data
  • 5. Wide Applications.
  • Disadvantages
  • 1. Data Acquisition
  • 2. Time and Resources
  • 3. Interpretation of Results
  • 4. High error-susceptibility.
  •  
  •  

20
Future scope
  • 1. Automatic translation of ideas/briefs/thoughts
  • 2. Traffic prediction at the highways/major roads
  • 3. Web search and recommendation engines (Search
    engines)
  • 4. Medical diagnosis (from past symptoms)
  • 5. Spam filtering in emails
  • 6. Recognition through images
  • 7. Virtual personal assistants (either in the
    office or to the remote students who dont have
    access to education).

21
Conclusions
  • Machine Learning Theory is both a
    fundamental theory with many basic and compelling
    foundational questions, and a topic of practical
    importance that helps to advance the state of the
    art in software by providing mathematical
    frameworks for designing new machine learning
    algorithms. It is an exciting time for the field,
    as connections to many other areas are being
    discovered and explored, and as new machine
    learning applications bring new questions to be
    modeled and studied. It is safe to say that the
    potential of Machine Learning and its theory lie
    beyond the frontiers of our imagination. 

22
References
  • 1 Dropout a simple way to prevent neural
    network from overfitting, by Hinton, G.E.,
    Krizhevsky, A., Srivastava, N., Sutskever, I.,
    Salakhutdinov, R. (2014). Journal of Machine
    Learning Research, 15, 1929-1958. (cited 2084
    times, HIC 142 , CV 536).
  • 2 Deep Residual Learning for Image Recognition,
    by He, K., Ren, S., Sun, J., Zhang, X. (2016).
    CoRR, abs/1512.03385. (cited 1436 times, HIC 137
    , CV 582).
  • 3 Batch Normalization Accelerating Deep
    Network Training by Reducing Internal Covariate
    Shift , by Sergey Ioffe, Christian Szegedy (2015)
    ICML. (cited 946 times, HIC 56 , CV 0).
  • 4 Large-Scale Video Classification with
    Convolutional Neural Network , by Fei-Fei, L.,
    Karpathy, A., Leung, T., Shetty, S., Sukthankar,
    R., Toderici, G. (2014). IEEE Conference on
    Computer Vision and Pattern Recognition (cited
    865 times, HIC 24 , CV 239).

23
  • 11Alpaydin, E. (2004). Introduction to Machine
    Learning. Massachusetts, USA MIT Press.
  • 12http//www.intechopen.com/books/new-advances-i
    n-machine-learning/types-of-machine-learning-algor
    ithms
  • 13 Carling, A. (1992). Introducing Neural
    Networks. Wilmslow, UK Sigma Press
  • 14 Friedberg, R. M. (1958). A learning machine
    Part, 1. IBM Journal, 2-13.
  • 15Mitchell, T. M. (2006). The Discipline of
    Machine Learning. Machine Learning Department
    technical report CMU-ML-06-108, Carnegie Mellon
    University.
  • 16 Richard S. Sutton, A. G. (1998).
    Reinforcement Learning. MIT Press.
  • 17 Ripley, B. (1996). Pattern Recognition and
    Neural Networks. Cambridge University Press.

24
  • 18 Tom, M. (1997). Machine Learning. Machine
    Learning, Tom Mitchell, McGraw Hill, 1997 McGraw
    Hill.
  • 19 Nilsson, N. J. (1965) Learning machines New
    York McGraw-Hill Popper.
  • 20 Rosenblatt, F. (1958) The perceptron a
    probabilistic model for information storage and
    organization in the brain Psychological Review.
  • 21 Michalski, R S , Carhoncll, .J G ,
    Mitchcll, T. M. (1983) (Eds) Machine Learning, an
    Artificial Intelligence Approach Palo Alto, CA
    Tioga Press.
  • 22 Anderson, J. A. (1983) Acquisition of proof
    skills in geometry.In R. S. Michalski, J. G.
    Carbonell T M Mitchell (Eds ), Machine
    Learnzng, An Artzficaal Intelligence Approach
    Palo Alto,

25
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