CS 4700: Foundations of Artificial Intelligence...--- reinforcement learning (intro)--- neural...
Transcript of CS 4700: Foundations of Artificial Intelligence...--- reinforcement learning (intro)--- neural...
1
CS 4700:Foundations of Artificial Intelligence
Prof. Bart [email protected]
Machine Learning:Neural Networks
R&N 18.7Backpropagation
2
3
4
5
6
7
8
Ok… details backpropagation NOT on exam.
But do work through gradient descent example on homework.
9
10
11
12
13
It’s “just” multivariate (or multivariable) calculus.
14
Optimizationandgradient descent.
Considerx and y ourtwo weightsand f(x,y)the errorsignal.
Multi-layernetwork:compositionof sigmoids;use chain rule.
Note:For descentwe want togo in oppositedirection ofgradient.
15
Reminder:Gradient descent optimization.(Wikipedia)
16
17
18
19
So, first we “backpropagate” the errorsignal to get the gradient, and then we update the weights, layer by layer.
That’s why it’s called “backprop learning.”Now changing speech recognition, imagerecognition etc.!
20
21
Trace example!
22
23
24
25
26
27
28
29
Etc.
Tedious but“just” gradientdescent in theweight space,after all.
30
Bla bla
Wall Street Journal, 11/24/16
Good news…
31
Summary J
A tour of AI:I) AI
--- motivation--- overview of the field
II) Problem-Solving–---- problem formulation using state space– (initial state, operators, goal state(s))–--- search techniques--- adversarial search
White white
32
Summary, cont. III) Knowledge Representation and Reasoning
--- logical agents--- Boolean satisfiability (SAT) solvers--- first-order logic and inference
V) Learning–--- decision tree learning (info gain)--- reinforcement learning (intro)--- neural networks / deep learning (gradient descent)
The field has grown (and continues to grow) exponentially but youhave now seen a good part!
Thanks & Have a great winter break!!!!