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CSTalks - On machine learning - 2 Mar
1.
2.
Introduction
3.
4.
5.
Before Learning I’m
learning, hence I need adapt!
6.
After Result: Liony
adjusts his diet.
7.
8.
9.
Motivation
10.
11.
12.
13.
14.
Important Problems
15.
Clustering
16.
Classification x1 x2
17.
18.
19.
Classification
20.
Regression
21.
22.
Learning Issues
23.
24.
25.
Segmentation
26.
Color Space RGB
space RGB space
27.
Color Space (cont)
28.
Suitable Clustering
29.
30.
31.
Learning Ability over
fitting
32.
Problems with
Over-fitting
33.
SVM vs Decision
Trees
34.
35.
36.
Training a Network
37.
Non-trivial Functions
38.
Optimization
39.
40.
41.
Gradient Descent
42.
Problem: Local Extrema
43.
Problem: Speed
44.
Linear Programming x1
x2 lines define a convex function planes in 3D etc
45.
46.
Open Problems
47.
48.
49.
The end Questions?
50.
51.
52.
Naive Bayes good
spam write people free π π No. Good No. Spam * *
53.
Graph Clustering
54.
Mean Shift
55.
56.
57.
Solution Stability y-shift
slope
58.
Some Issues with
Model Selection normal outliers wrong model
59.
Real Photo in
Color Space EM KMeans
60.
Conjugate Gradient
61.
Newton’s Method
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