Maximum Likelihood Estimation
.
The Setting
We have a probabilistic model, M, of some
phenomena. We know exactly the structure of
M, but not the values of its probabilistic
parameters, .
Each execution of M produces an
observation, x[i] , according to th

2015 IEEE International Conference on Bioinformatics and Biomedicine (BTBM)
Systematic Analysis of Machine Learning Algorithms
on EEG Data for Brain State Intelligence
*
*
Alexander Chan l, Christopher E. Early 2, Sishir Subede, Yuezhe Li4, Hong Lin3
'Dep

Supervised Learning
Road Map
Basic concepts
Decision tree induction
Evaluation of classifiers
Naive Bayesian classification
Nave Bayes for text classification
Support vector machines
K-nearest neighbor
Summary
2
Supervised vs. unsupervised Learning
Super

Reinforcement Learning
Learning Types
Supervised learning:
(Input, output) pairs of the function to be learned can
be perceived or are given.
Back-propagation in Neural Nets
Unsupervised Learning:
No information about desired outcomes given
K-means cl

Dimensionality Reduction
Curse of Dimensionality
A major problem is the curse of dimensionality.
If the data x lies in high dimensional space, then
an enormous amount of data is required to learn
distributions or decision rules.
Example: 50 dimensions.

Recommended References
1. Pattern Recognition and Machine Learning,
C. M. Bishop, Springer, Oct. 2007.
2. Pattern Classification, R. O. Duda, P. E. Hart,
D. G. Stork, Wiley-Interscience; 2nd Edition,
Nov. 2000.
3. Neural Networks and Learning Machines,
Si

Probability Distribution
Random Variable
A random variable x takes on a defined set of
values with different probabilities.
For example, if you roll a die, the outcome is random (not
fixed) and there are 6 possible outcomes, each of which occur
with pro

Lab 10 Report
1. Xilinx Design suite is opened and Project by name Lab_10 is created.
2. The VHDL code for the program is written as shown below:
3. The UCF file for the VHDL is also written as shown below:
4. Then the programming file has been generated.