203.4770 (3770)
Semester B
Meeting
Times: Monday 10-12 ;
Thursday 10-12
Location: Room
Machine learning is concerned with the development of computer algorithms that are able to learn solving tasks given a set of examples of those tasks and some prior knowledge about them. Machine learning has a wide spectrum of applications including handwritten or speech recognition, image classification, medical diagnosis, stock market analysis, bioinformatics etc. The goal of this course is to present the main concepts of modern machine learning methods including some theoretical background.
Recommended Prerequisites
The course assumes some basic knowledge of probability
theory and linear algebra;
for example, you should be familiar with
Tutorials of the above topics.
Problems, Concepts, Methods, and Tools within in the
course
The list is partial and be can changed.
Problems
Concepts
Models and Methods
Tools
The course will furthermore use several real-life applications to illustrate the interest of statistical machine learning.
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Grades of home exam are available here.
hazara TBD
Both assignments should be submitted on 17/08!
Home exam will be sent out on 25/08 (morning) and
should be submitted by 26/08, 23:55.
Home Assignment 2 is available. The data needed for this assignment
can be downloaded here.
Home Assignment 1 is available. The problem set will be distributed
by email, send a request to e-mail: rita
[at]cs [dot]haifa.ac.il. The data
needed for this assignment can be downloaded here.
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12.5 |
Introduction |
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15.5 |
Probability Tutorial, Introduction to
Classification |
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19.5 |
Bayesian Decision
Theory, ML, MAP classifiers |
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22.5 |
Tutorial on Bayesian Decision
Theory, ML, MAP classifiers |
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26.5 |
Normal Variables and
their discriminant functions |
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29.5 |
Parametric density
estimation MLE MLE - tutorial |
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2.6 |
Parametric density
estimation - Bayesian Estimation |
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5.6 |
Naïve Bayes Non-parametric density
estimation, Histogram, Parzen Window |
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9.6 |
shavuot |
|
12.6 |
Non-parametric density
estimation, nearest neighbors, KNN. |
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16.6 |
LDF |
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19.6 |
MSE |
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23.6 |
SVM (guest lecture) |
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26.6 |
Intro to Neural Networks(guest
lecture) |
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30.6 |
Dimensionality
Reduction PCA |
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3.7 |
FDA,MDA |
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7.7 |
Linear Regression, Regression
with Shrinkage |
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10.7 |
Bias-Variance
Decomposition Linear |
|
14.7 |
Computational Learning
Theory |
|
17.7 |
Decision Trees Clustering |
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21.7 |
Clustering, EM |
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25.7 |
Boosting |
|
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- All home assignments will be distributed by email. Send a request to e-mail: rita [at]cs [dot]haifa.ac.il
-
Each
assignment should be formatted as a report (only outputs, plots etc; no code).
-
Submissions
via email or printed (dont print your code).
Data for Assignment 2:
The data contains a face
data, two m-files for visualization, and a plant data. All files are archived
in Data.zip
Data for Assignment 1:
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http://www.autonlab.org/tutorials/prob18.pdf
- first half
http://www-stat.stanford.edu/~susan/courses/s116/
Linear Algebra tutorial:
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Introductory Tutorials
MATLAB
tutorial from University of Utah
MATLAB tutorial
from Carnegie Mellon University
MATLAB
tutorial from Indiana University
Slightly more advanced Tutorials
More complete references/tutorials/FAQs
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