Hidden Markov models are probabilistic frameworks that model observed data as a series of outputs generated by hidden internal states over time. They can be used for problems like speech recognition, where the observable speech is used to deduce the hidden internal state of the script. A first-order Markov process assumes the future state solely depends on the current state, and can be represented by a transition matrix. Hidden Markov models are useful for solving complex machine learning and reinforcement learning problems involving hidden states.

1. Hidden
Markov Model
By. Mohamed Essam

2. Hidden Markov Model
Markov and Hidden Markov models are engineered
to handle data which can be represented as
‘sequence’ of observations over time. Hidden
Markov models are probabilistic frameworks
where the observed data are modeled as a series
of outputs generated by one of several (hidden)
internal states.

3. Hidden Markov Model
How do you know your Wife is happy or not? Any couple
will tell you it can be hard. In machine learning ML,
many internal states are hard to determine or observe.
An alternative is to determine them from observable
external factors.
That is what HMM solves.

4. Hidden Markov Model

5. Hidden Markov Model

6. Hidden Markov Model

7. Hidden Markov Model

8. Hidden Markov Model

9. Hidden Markov Model

10. Hidden Markov Model
For example, in speech recognition, we listen to a speech
(the observable) to deduce its script (the internal state
representing the speech).

11. Hidden Markov Model
For example, in speech recognition, we listen to a speech
(the observable) to deduce its script (the internal state
representing the speech).

12. Hidden Markov Model
For example, in speech recognition, we listen to a speech
(the observable) to deduce its script (the internal state
representing the speech).

13. Hidden Markov Model
For example, in speech recognition, we listen to a speech
(the observable) to deduce its script (the internal state
representing the speech).

14. Hidden Markov Model
A first-order Markov process is a stochastic process
in which the future state solely depends on the
current state only. The first-order Markov process
is often simply called the Markov process. If it is in
a discrete space, it is called the Markov chain.

15. Hidden Markov Model
A first-order Markov process is a stochastic process
in which the future state solely depends on the
current state only. The first-order Markov process
is often simply called the Markov process. If it is in
a discrete space, it is called the Markov chain.

16. Hidden Markov Model
A first-order Markov process is a stochastic process
in which the future state solely depends on the
current state only. The first-order Markov process
is often simply called the Markov process. If it is in
a discrete space, it is called the Markov chain.

17. Hidden Markov Model
A first-order Markov process is a stochastic process
in which the future state solely depends on the
current state only. The first-order Markov process
is often simply called the Markov process. If it is in
a discrete space, it is called the Markov chain.

18. Hidden Markov Model
the Markov process can be an appropriate
approximation in solving complex ML and
reinforcement learning problems. In addition, the
probability of the transition from one state to
another can be packed into a transition matrix like
the one below:

19. Hidden Markov Model

20. Any Questions?
Mohamed Essam
!

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THANKS!
Contacts
Mhmd96.essam@gmail.com
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