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Lecture notes for Computational Intelligence

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Summarizes all of the contents discussed in all the lectures

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  • May 29, 2021
  • 31
  • 2020/2021
  • Class notes
  • Jakub tomczak
  • All classes
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,Lecture 1 : introduction




Intelligence and computations

Intelligence : the ability to acquire and
apply knowledge and problem solving
computations :




the action mathematical calculation
'




of

the computers
-


use of
data)
knowledge :
experience ( =
represented by facts and information

computational intelligence :
computers acquire knowledge and solve problems




Artificial intelligence vs .
Computational intelligence
symbolic Al Neural networks
-
-




logic knowledge representations Evolutionary algorithms
'


. . .

,




sub -


symbolic A1 : Nature -

inspired algorithms

intelligence
'


neural nets swarm
-
- -
.




evolutionary algorithms Probabilistic methods
'




inspired Optimization
'

nature -


algorithms
statistical learning
probabilistic methods

optimization


in the end ,
it's almost the same




is ?
why As successful

Accessible and hardware
-



powerful

intuitive programming languages +
specialized packages
-




components of Al ICI systems
'




knowledge representation :
how to represent and process data ?


knowledge acquisition ( learning objective &
algorithms) knowledge ?
:
now to
'



extract


?
Learning problems what kind problems we formulate
'

:
of can




Optimization

Find optimal ( min Max ) possible
the solution or from a
given set of solutions

that minimizes I maximizes
given objective function


in order to save the problem ,
we need a numerical


algorithm

,Learning as optimization
For given data find best representation from
, the data a
given clan of

representations that minimizes given learning objective Closs ) .




optimization algorithm
=
learning algorithm




Learning tasks


supervised learning

distinguish inputs and outputs
>




-


interested in prediction
-
minimize a prediction er ror




Unsupervised learning
'

no distinction

structure
-


look for a data


-

minimize a reconstruction error , compression rate . . . .




Reinforcement learning
-

an agent interacts with an environment


want to learn policy
'

a



rewarded
-




each action is

-


maximize a total reward

, Lecture 2 :
optimization



Optimization
Find optimal solution ( minimum maximum) possible
the or from a
given set of

solutions that minimizes 1 maximizes given objective function

Formally :




Remarks :




'


min f- Cx) =
Max E -



f Cx) }
'S
's
E. X R X CO I }
-
=

g
=
.

. , ,




-




optimal solution :
Fae × fCn* ) E f Cn) and tic ; Cx )
*
f b;




Optimization problems
Taxonomy of

'

constrained 1 Unconstrained


convex 1 Non convex
-
-




Deterministic 1 stochastic
'




continuous 1 Discrete
-




-

Global 1 Local




optimization Methods

three main classes :




Derivative methods can methods)
-
-

free order


does mathematical definition constraint is and
>



not need a , a placed

the problem is optimized from there


e
g hill climbing iterated local
-


-
.
,
search


Gradient ( 1st )
'

-
based methods order methods


use information gradient objective function working top
-



about of , from the


downwards


gradient ADAM
-




e.g .
descent ,




Hessian (2nd order methods)
-

-

based methods


require to calculate nesn
-




e
g Newton 's method
-

-
.




Iterative optimization methods


interested numerical methods
'

in



Therefore consider iterative optimization methods
-




,

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