# نظريات المكائن في عدة مشاركات !!!!!!!!!!!نظريات المكائن في عدة مشاركات



## حسن هادي (23 أبريل 2007)

Information Theory and
Machine Learning​T-61.182 Special Course in Computer and
Information Science II, Spring 2004 (4 cr)
Prof. Juha Karhunen
Helsinki University of Technology​1
Information Theory and
Machine Learning
T-61.182 Special Course in Computer and
Information Science II, Spring 2004 (4 cr)
Prof. Juha Karhunen
Helsinki University of Technology
1
– Problems have been classified in the book: 1= simple,
2=medium, 3=moderately hard, 4=hard, 5=research project.
– You should select only problems having degrees 1, 2, or 3.
– Problems are useful because they force people to read the
corresponding parts of the book.
– It is preferable but not necessary to return your solutions to the
problems given within 2 weeks.
– Deadline for returning solved problems: May 15th, 2004.
– Somewhat open issue: replacing some problems by computer
assignment(s) giving hands-on experience!?
9​ 
​


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## حسن هادي (23 أبريل 2007)

TENTH WORLD CONGRESS ON THE THEORY OF
MACHINE AND MECHANISMS​Oulu, Finland, June 20 – 24, 1999​A FORCE-UPDATED KINEMATIC VIRTUAL VIEWING SYSTEM WITH
APPLICATION TO NUCLEAR POWER PLANT MAINTENANCE​Marco A. Meggiolaro, Peter C. L. Jaffe, Karl Iagnemma and Steven Dubowsky​Department of Mechanical Engineering
Massachusetts Institute of Technology
Cambridge, MA 02139​_Abstract​_Important nuclear maintenance tasks, such as the steam generator nozzle dam placement, could
be most effectively done by robotic systems. However, restricted teleoperator visibility and lack
of absolute accuracy make such tasks very difficult to perform using conventional robotic control
technology. In this paper, successful nozzle dam placement is achieved through a 3-D virtual
viewing system which includes contact force information.​_Keywords: contact forces, Base Sensor Control, virtual viewing, nuclear maintenance, robotics​_1 Introduction​Most nuclear power plant maintenance tasks are currently performed by humans. Some of these
tasks, including the important placement of the steam generator nozzle dam, require workers to
be exposed to dangerously high levels of radiation [Zezza 1985]. To avoid human exposure,
robotic systems have been proposed and evaluated for such tasks. However, in a number of
cases, the robotic technology was not adequate. In this paper, a robotic system for successfully
placing a nuclear power plant steam generator nozzle dam is presented. Nozzle dam placement
is a challenging and typical nuclear maintenance robotic task. Restricted teleoperator visibility
and tight geometric tolerances between the nozzle dam and its receptacle make the task very
difficult to perform using conventional robotic control technology. Our approach consists of a
virtual viewing system based on 3-D kinematic models combined with real-time contact force
measurements. Laboratory experiments show that successful nozzle dam placements could be
performed using the visualization system with a conventional Schilling hydraulic manipulator.​2 Task and Experimental System​2.1 A Representative Task
The control system presented in this paper has been developed for a nuclear maintenance task.
In order for workers to inspect and repair a nuclear power plant’s steam generator, two large
pipes (1 meter in diameter) must be sealed with a device called a nozzle dam. The nozzle dam
weighs 60 kg and must be inserted into a nozzle ring with clearances of approximately one
millimeter. Workers enter the steam generator through a 0.4 meter in diameter portal and receive
high doses of radiation while securing the nozzle dam. Hence, performing this task with a
robotic manipulator would be very desirable. A simulated robotic nozzle dam placement can be
seen in Figure 1a, where the manipulator is moving the nozzle dam side plate into its position in
the nozzle ring. The center plate is then inserted within the side plate as shown in Figure 1b.
Past attempts to place the nozzle dam with a teleoperated manipulator have taken too long
because of the combination of poor operator visibility and the lack of manipulator repeatability.
Tens of thousands of dollars of revenue are lost each hour the nuclear power plant is offline. In
this paper, both the operator interface and manipulator repeatability are improved to make
automated nozzle dam insertion practical.​Side Plate
Center Plate
Nozzle Ring
Manipulator​(a)​Nozzle Dam
Center Plate
Nozzle Dam
Side Plate​(b)​Figure 1 - Simulated Robotic Nozzle Dam Task​2.2 Experimental System
Figure 2 shows the laboratory system used to emulate one of the critical parts of the nozzle dam
placement task. Figure 3 shows an 18 kg nozzle dam center plate and a variable tolerance
receptacle used to emulate the insertion of the nozzle dam center plate into the side plate. The
receptacle is mounted in the workspace so that the manipulator configurations are representative
of the actual task. A handle on the center plate provides a repeatable grip for the manipulator.
The manipulator chosen for this task is a Schilling Titan II six DOF hydraulic robot capable of
handling payloads in excess of 100 kg. This manipulator is widely used in nuclear maintenance.​Figure 2 - System Hardware Configuration
(a) (b)
Figure 3 – Nozzle Dam Center Plate (a) and Variable Tolerance Receptacle (b)​Joint resolver signals, standard on the Schilling, are converted to quadrature encoder waveforms
using a Delta Tau Data/PMAC controller design. Both base and wrist force/torque sensors are
sampled by a Data Translation 16-bit ADC. A 300 MHz PC handles the control loop
computations and graphical user interface.
In order to perform the nozzle dam placement task, a custom teleoperator software package has
been developed. The system contains 3-D kinematic models of the manipulator and the
workspace, reflecting the actual system configuration based on the joint resolvers. The interface
provides improved operator visibility by allowing “virtual viewing” of physically obscured
regions using “virtual cameras” [Cho, 1998]. The virtual cameras also allow for magnifying the
mating edges in order to aid in teleoperated insertion. A Cartesian end-point controller is
embedded in the software to provide full teleoperation functionality. Figure 4 shows the
manipulator and experimental testbed for both real and simulated systems.​(a) (b)
Figure 4 – Real (a) and Simulated (b) Experimental System​The tight tolerances of the task require the teleoperator to command fine position adjustments.
However, the strong manipulators required for such tasks lack repeatability, mainly due to high
friction in their joints and actuators. Base Sensor Control (BSC) [Morel et al. 1996] is
implemented to improve the manipulator repeatability through accurate joint torque control.
Furthermore, manipulator end-point errors due to geometric distortions of the system and elastic
deformations degrade the manipulator’s accuracy. Here a method called Geometric and Elastic
Error Compensation (GEC) is used to greatly reduce these errors [Meggiolaro et al. 1998].
However, even with these key enabling technologies, some geometric uncertainty still exists
between the modeled and real environments making teleoperation difficult to perform. To
overcome this, the contact forces between the center plate and its receptacle are estimated from
wrist sensor and task geometry and displayed to the teleoperator.​3 Contact Force Estimation​Contact force information between the manipulator end-effector and the environment is
fundamental for placement tasks with small tolerances [Bicchi 1993]. However, a wrist
force/torque sensor provides limited information, namely 3 force and 3 torque components, while
each contact point is associated with 9 unknowns: the coordinates of the contact point location
and the contact wrench components. In the case where there is only one contact point with the
environment and where the contact torque is zero, it is possible to calculate the contact
information required for control. This can be obtained from wrist force torque information
combined with knowledge of the geometry of the mating parts.
Figure 5 shows a plate attached to the end-effector of a manipulator. The force exerted by the
environment on the plate,​Fc, and the contact location with respect to the wrist force/torque
sensor, rc, can be calculated from:​
s
s
s
s s
s s
c s c​​F
F
M
F M
F M
F F r + a
´
´
= , = (1)
where Fs and Ms are respectively the forces and moments measured from the wrist sensor, and a​
is an arbitrary constant. Note that Equation (1) has an infinite number of solutions, since two
equal forces along the same line of action result in the same wrist sensor reading, see Figure 5.​Figure 5 - Contact Force and Wrist Force/Torque Sensor Readings​In order to obtain a unique solution to Equation (1), the plate geometry must be considered.
Defining​G as a vector function representing the plate surface in the wrist sensor coordinates,
then a is determined by calculating the intersection between the line of action of the contact
force and the plate surface G,​
s
s
s s
s s
s​​F
M
F M
F M
F​
´
´
a º​G - (2)
Due to the nature of contact forces, which are directed toward the interior of the plate, the
calculated values of rc must also satisfy​
n​(rc ) ×Fc £ 0 (3)
where n(rc) is the normal vector to the plate surface at the point rc.
If G represents a convex surface, then the solution to Equations (2) and (3) is either unique or
non-existent. Otherwise, multiple solutions exist for certain configurations. For the particular
case shown in Figure 5, G is not convex, but it can be represented by a set of simple equations of
the planes of the plate. Frequently, as in the case of the nozzle dam insertion plate, a single
solution for the contact point can be determined by considering the contact friction as well as the
geometry of the mating parts.
Based on Equations (2) and (3) and models of the plate and receptacle, a force vector and contact
point is calculated from the measured wrist wrench and displayed to the teleoperator. Figure 6
summarizes how a force-updated operator interface is combined with the high accuracy BSC
position controller to perform the nozzle dam placement task.​
Desired joint
variables
Actual joint positions
Base Force/
Torque Sensor
BSC Robot​-​+​q
q​des​X​Inverse
kinematics
Desired
End-Effector
Position​des​Contact Force
Estimation
Plate Geometry
Visualization
Software
Teleoperator
Wrist Force/
Torque Sensor​Figure 6 - Base Sensor Control and Contact Force Estimation Scheme​4 Experimental Verification​Representative nozzle dam placements were conducted to demonstrate the effectiveness of the
force-updated virtual viewing system. Figure 7 shows a sequence of screenshots from the
teleoperator display during a typical placement. Each figure shows the center plate contacting
the mating receptacle as well as visual feedback of the estimated contact force. The contact
vector identifies misalignments in the insertion process, providing the necessary information to
command small corrective motions.
Figure 7a suggests translational motions are necessary to align the plate. The next four
screenshots shown in Figure 7 indicate rotational alignment errors. Finally, the contact force in
Figure 7f suggests that successful placement was achieved.​(a) (b) (c)
(d) (e) (f)
Figure 7 – Typical Placement Steps Using Contact Force Visualization​The experimental insertions show that the force-updated virtual viewing system outlined in
Figure 6 allows a conventional hydraulic manipulator to successfully perform the nozzle dam
placement task. This approach is made practical by the means of BSC and GEC Control.​5. Conclusions​In this paper, a robotic visualization system for successfully placing a nuclear power plant steam
generator nozzle dam is presented. A teleoperator software package has been developed
containing 3-D kinematic models of a Schilling Titan II hydraulic manipulator and the
workspace. Contact force information between the center plate and its receptacle is obtained
from wrist sensor wrench measurements and geometric models of the mating geometries. The
contact force vector is displayed to the teleoperator and allows for real-time recognition of
misalignments in the insertion process. This aids in successfully achieving insertion using a
position control algorithm. Experiments demonstrated that the nozzle dam placement task can be
achieved by combining a high repeatability position controller, such as BSC and GEC, and a
force-updated operator interface.​Acknowledgments:​The assistance and encouragement of Dr. Byung-Hak Cho of the Korean Electric Power
Research Institute (KEPRI) and Mr. Jacque Pot of the Electricité de France (EDF) in this
research is most appreciated, as the financial support of KEPRI and EDF.​References:​Bicchi, A., Salisbury, J., Brock, D., Contact Sensing from Force Measurements, The
International Journal of Robotics Research, Vol. 12, No. 3, 1993, pp 249-262.
Cho, B., Simulation Studies on Robot System Applied to Nozzle Dam Installation, KEPRI
Technical Memo, 1998.
Iagnemma, K., Morel, G. and Dubowsky, S., A Model-Free Fine Position Control System Using
the Base-Sensor: With Application to a Hydraulic Manipulator, Symposium on Robot Control,
SYROCO ‘97, Vol. 2, 1997, pp 359-365.
Meggiolaro, M., Mavroidis, C. and Dubowsky, S., Identification and Compensation of
Geometric and Elastic Errors in Large Manipulators: Application to a High Accuracy Medical
Robot, Proceedings of the 1998 ASME Design Engineering Technical Conference, Atlanta,
1998.
Meggiolaro, M., Jaffe, P. and Dubowsky, S., "Achieving Fine Absolute Positioning Accuracy in
Large Powerful Manipulators", submitted to the IEEE International Conference on Robotics and
Automation (ICRA'99), Detroit, 1999.
Morel, G. and Dubowsky, S., The Precise Control of Manipulators with Joint Friction: A Base
Force/Torque Sensor Method, Proceedings of the IEEE International Conference on Robotics
and Automation, Vol. 1, 1996, pp 360-365.
Zezza, L., Steam Generator Nozzle Dam System”, Transactions of the American Nuclear
Society, Vol. 50, 1985, pp 412-413.​


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## حسن هادي (23 أبريل 2007)

http://robots.mit.edu/people/Karl/iftomm99.PDF
استخدم الرابط للحصول على معلومات افضل


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## حسن هادي (23 أبريل 2007)

*Bioscience Chapter Database :: 3093 Chapters Now Online *
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*Chapter category: Nanomedicine*

*CHAPTER 2 Classical Theory of Machine Replication*

This chapter appears in the following book: 
*Kinematic Self-Replicating Machines*

Edited by: *Robert A. Freitas, Jr. and Ralph C. Merkle*
ISBN: 1-57059-690-5
» Get more information about this book at landesbioscience.com « 
Chapter authors: 
*Robert A. Freitas Jr. and Ralph C. Merkle*





[+] view image​The early history of machine replication theory is largely the record of von Neumann’s thinking on the matter during the 1940s and 1950s, particularly his kinematic and cellular models, described below. Von Neumann did not finish or publish most of his work on this subject prior to his untimely death in 1957, but Arthur Burks, a colleague of von Neumann, extensively edited and completed many of von Neumann’s manuscripts on the subject. Automata theory has advanced and been refined in the decades since, with many alternative models of machine replication having been proposed and discussed as will be described later. By 1980, a detailed technical study co-edited by Freitas2 concluded that “there appear to be no fundamental inconsistencies or insoluble paradoxes associated with the concept of self-replicating machines.” Physics professor Jeremy Bernstein concurred:1040 “I believe, on the basis of the history of technology, that human nature is such that whatever can be constructed, in theory, will, eventually, be constructed. Since self-replicating automata are possible in principle, they will, I think, eventually be built. When, by whom, and what for, I do not have the foggiest idea.” 
» Add chapter to custom book
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*Additional chapters from this book:*


*CHAPTER 1 The Concept of Self-Replicating Machines*

Robert A. Freitas Jr. and Ralph C. Merkle
For most of human history, man’s tools and machines bore no resemblance to living organisms and gave no hint of any commonality between the living and the artificial.150 In Paleolithic times,151-15...</B>

*CHAPTER 2 Classical Theory of Machine Replication*

Robert A. Freitas Jr. and Ralph C. Merkle
The early history of machine replication theory is largely the record of von Neumann’s thinking on the matter during the 1940s and 1950s, particularly his kinematic and cellular models, described b...</B>

*CHAPTER 3 Macroscale Kinematic Machine Replicators*

Robert A. Freitas Jr. and Ralph C. Merkle
Specific proposals and realizations of von Neumann’s kinematic replicators and related physical implementations of macroscale machine replicators or self-replicating factory systems are of the grea...</B>

*CHAPTER 6 Motivations for Molecular-Scale Machine Replicator Design*

Robert A. Freitas Jr. and Ralph C. Merkle
In 1959, Feynman2182 proposed that we could arrange atoms in most of the ways permitted by physical law. Von Neumann3 analyzed a few basic architectures for self-replicating systems in the 1940s an...</B>

*APPENDIX A Data for Replication Time and Replicator Mass*

Robert A. Freitas Jr. and Ralph C. Merkle
Data for replication time (τ) as a function of replicator mass (M) for 126 biological species,2600 1 chemical species,1372 and 9 actual or proposed artificial kinematic replicating systems acr...</B>

*APPENDIX B Design Notes on Some Aspects of the Merkle Freitas Molecular Assembler*

Robert A. Freitas Jr. and Ralph C. Merkle
Geometrical Derivation of Assembler Dimensions A preliminary design iteration revealed that the physical dimensions of the proposed molecular assembler are constrained by the choice of 4 box-specif...</B>



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## حسن هادي (23 أبريل 2007)

http://www.eurekah.com/chapter/2395
الرابط للاستخدام


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## حسن هادي (23 أبريل 2007)

*Bayesian Decision Theory and Machine Learning 
*

*Kathryn Blackmond Laskey
Department of Systems Engineering
George Mason University 
*

This talk presents the Bayesian approach to machine learning. The talk begins with an overview the basic philosophy and approach of Bayesian decision theory. Next, application of the decision theoretic approach to machine learning is discussed. In decision theory, learning is viewed as a problem of inference, in which a prior distribution and data are used to infer a posterior distribution for parameters of interest. Problems in machine learning may be contrasted with more traditional statistical inference problems. Machine learning problems are characterized by very high dimensional parameter spaces and by models that are not "identifiable" -- that is, it may not be possible to distinguish on the basis of available training data which of several candidate representations is the "correct" one. This suggests an alternative characterization of the machine learning problem in decision theoretic terms. The learning task is viewed as acquiring a problem representation that has high utility, where utility depends both on accuracy (i.e., projected performance on problems not in the training set) and computational complexity. Theoretical and pragmatic arguments for Bayesian methods are presented. A summary of recent research in knowledge representations and learning methods is presented. A few applications of Bayesian learning are discussed. 
View Presentation Download Presentation 


 Back to Lectures List 
********s*

Bayesian Decision Theory 
Decision Theory and
Decision Theory
Bayesian Inference
A Caricature of a Contrast
Machine Learning 
Graphical Models 
Learning for High-Dimensional
Structural Uncertainty
Approaches to Structural Uncertainty
Higher Order Uncertainty 
Learning about Structure
Some Examples
Advantages to Model Averaging
More Advantages
Criticisms
Issues
Decision Theory
Occam's Razor
Occam's Razor (cont.)
Decision Theory and Occam's Razor
Summary


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## حسن هادي (23 أبريل 2007)

بامكانك استخدام الروابط


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## حسن هادي (23 أبريل 2007)

http://www.cs.cmu.edu/~avrim/ML98/home.html
رابط


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## حسن هادي (23 أبريل 2007)

الله يساعدكم


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## حسن هادي (23 أبريل 2007)

*Thoughtfully reflect on the new video: "God, Mind, Truth" - Part One *


*"God, Mind, Truth" - Part Two *

*To Purchase*


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## حسن هادي (23 أبريل 2007)

*[SIZE=+1]Click on the titles below to view these important documents:[/SIZE]*

** The Origins of the Patent Battle *
* A New Paradigm *
* An Interesting Demonstration *
* A Cooling Effect *
* Corroborative Information from The Journal of Applied Physics *
* Endorsement of Joseph Newman's Work by Distinguished Expert *
* Affidavits & Evaluation *
* Joseph Newman's Statement to Universities *
* Declaration by Dr. Roger Hastings, PhD *
* Statement by Joseph Newman *
* Four Letters from a Mathematical Physicist *
* Heat & The Three Laws of Thermodynamics *
* Joseph Newman's Theory --- by Dr. Roger Hastings, PhD *
* Letter from Col. Thomas Bearden *
* A Preliminary Quantification of Newman's Effect *
* Design Considerations for Rotating Magnet Newman Motors *
* Measurement and Analysis of Joseph Newman's Energy Generator *
* Commentary Regarding Einstein's Equation of E = mc^2 *
* The Magnetic Current and Single Magnetic Charges *
* Existence of "Less-Than-Whole" Electronic Charges" *
* Light and Quantum Mechanics: Additional Verification *
* Falling Gyroscope Experiment: Additional Verification **


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## حسن هادي (23 أبريل 2007)

*Machine Learning Theory (Weizmann course, Fall 2006-7) *

In theory, this course will cover models and algorithms for machine learning. What classes of functions are learnable and what algorithms should be used? Where possible, we will try to prove appealing properties of algorithms that are also useful in practice. This course will also demystify many buzzwords such as: PAC learning, VC dimension, Boosting, Fourier Techniques, Statistical Queries, Parity with Noise, Decision Tree learning, Support Vector Machines, and the Multi-Armed Bandit Problem. 

Note: Another course being offered this semester is Probabilistic Graphical Models by Eran Segal. His course is disjoint from this course and would be excellent to take in conjunction. *Course info *

Location: Weizmann Institute, Ziskind Room 1 
Time: Tue. 14:00-16:00, Oct. 31, 2006-Feb. 6, 2007 
Instructor: Adam Tauman Kalai. 
Grader: Ariel Gabizon. 

This course is partly based on an excellent Machine Learning Theory course taught by Avrim Blum at CMU. 
*Mailing list*

The course mailing list is here. You can join or simply read the messages. 
*Recommended textbooks*

Michael Kearns and Umesh Vazirani. _An introduction to computational learning theory_. 
Tom Mitchell. _Machine Learning_. (More applied) 
Trevor Hastie, Robert Tibshirani, and Jerome Friedman. _The elements of statistical learning_. (More statistics-oriented) 
*Homework*

Homework should be turned in either in class or may be put in the MACHINE LEARNING course mail box, which is on the second floor of Ziskind. 
Problem set 1 is now available. Due Nov. *28*, 2006. Problem set 1 hint is now available. 
Problem set 2 is now available. Due Dec. *14*, 2006. 
Problem set 3, theory or implementation is now available. You have your choice. Due Jan. *11*, 200*7*. 
Problem set 4 is now available. Due Feb. *8*, 2007. (Clarifications added 8/2/07.) 
*Lectures*

This schedule is certain to change. 
*Introduction and online learning*

*Oct 31.* Introduction. Learning an OR via the WINNOW algorithm, and online adaptation. An interesting article, The discipline of machine learning by Tom Mitchell. 
*Nov 7.* The Weighted Majority and Perceptron algorithms. An interesting related paper is the following:
Smoothed Analysis of the Perceptron Algorithm for Linear Programming by Avrim Blum and John Dunagan. Proceedings of the thirteenth annual ACM-SIAM symposium on Discrete algorithms, (SODA 2002), pp. 905-914, 2002. 
*Nov 14.* Holiday. 
*Batch learning*

*Nov 21.* Batch learning, online to batch conversion, and Occams razor. See also the handout on tail inequalities from Avrim Blum. Slides are available here. 
*Nov 28.* VC dimension. Slides are available. 
*Dec 5.* No class. See interesting related lecture on Sunday, Dec. 10. 
*Dec 12.* Boosting: AdaBoost. Here is a survey of boosting, and some excellent slides by Schapire. 
*Dec 19.* Decision trees and Noisy/Real boosting. Slides are available. *Note, course meets in Ziskind 261.* 
*Dec 26.* Completion of real-valued boosting and decision regression graphs, and additive models. Last weeks notes plus this weeks slides cover this week. 
*Jan 2.* Class *canceled*. 
*Jan 9.* Statistical queries and learning parity with noise. 
*Jan 16.* Support vector machines and fourier methods. We will use slides by Martin Law. 
*Jan 23.* SVMs, Fourier, and Agnostic Learning. 
*Other models of learning*

*Jan 30.* Online optimization. See paper by Zinkevich. For extension to the bandit setting, see this paper. 
*Feb 6.* Learning in repeated games. See Regret in the On-line Decision Problem by Foster and Vohra for a great introduction. To see the specific reduction from external to internal regret I was talking about, see From External to Internal Regret by Avrim Blum and Yishay Mansour.


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## حسن هادي (23 أبريل 2007)

*Machine Learning Theory (Weizmann course, Fall 2006-7) *

In theory, this course will cover models and algorithms for machine learning. What classes of functions are learnable and what algorithms should be used? Where possible, we will try to prove appealing properties of algorithms that are also useful in practice. This course will also demystify many buzzwords such as: PAC learning, VC dimension, Boosting, Fourier Techniques, Statistical Queries, Parity with Noise, Decision Tree learning, Support Vector Machines, and the Multi-Armed Bandit Problem. 

Note: Another course being offered this semester is Probabilistic Graphical Models by Eran Segal. His course is disjoint from this course and would be excellent to take in conjunction. *Course info *

Location: Weizmann Institute, Ziskind Room 1 
Time: Tue. 14:00-16:00, Oct. 31, 2006-Feb. 6, 2007 
Instructor: Adam Tauman Kalai. 
Grader: Ariel Gabizon. 

This course is partly based on an excellent Machine Learning Theory course taught by Avrim Blum at CMU. 
*Mailing list*

The course mailing list is here. You can join or simply read the messages. 
*Recommended textbooks*

Michael Kearns and Umesh Vazirani. _An introduction to computational learning theory_. 
Tom Mitchell. _Machine Learning_. (More applied) 
Trevor Hastie, Robert Tibshirani, and Jerome Friedman. _The elements of statistical learning_. (More statistics-oriented) 
*Homework*

Homework should be turned in either in class or may be put in the MACHINE LEARNING course mail box, which is on the second floor of Ziskind. 
Problem set 1 is now available. Due Nov. *28*, 2006. Problem set 1 hint is now available. 
Problem set 2 is now available. Due Dec. *14*, 2006. 
Problem set 3, theory or implementation is now available. You have your choice. Due Jan. *11*, 200*7*. 
Problem set 4 is now available. Due Feb. *8*, 2007. (Clarifications added 8/2/07.) 
*Lectures*

This schedule is certain to change. 
*Introduction and online learning*

*Oct 31.* Introduction. Learning an OR via the WINNOW algorithm, and online adaptation. An interesting article, The discipline of machine learning by Tom Mitchell. 
*Nov 7.* The Weighted Majority and Perceptron algorithms. An interesting related paper is the following:
Smoothed Analysis of the Perceptron Algorithm for Linear Programming by Avrim Blum and John Dunagan. Proceedings of the thirteenth annual ACM-SIAM symposium on Discrete algorithms, (SODA 2002), pp. 905-914, 2002. 
*Nov 14.* Holiday. 
*Batch learning*

*Nov 21.* Batch learning, online to batch conversion, and Occams razor. See also the handout on tail inequalities from Avrim Blum. Slides are available here. 
*Nov 28.* VC dimension. Slides are available. 
*Dec 5.* No class. See interesting related lecture on Sunday, Dec. 10. 
*Dec 12.* Boosting: AdaBoost. Here is a survey of boosting, and some excellent slides by Schapire. 
*Dec 19.* Decision trees and Noisy/Real boosting. Slides are available. *Note, course meets in Ziskind 261.* 
*Dec 26.* Completion of real-valued boosting and decision regression graphs, and additive models. Last weeks notes plus this weeks slides cover this week. 
*Jan 2.* Class *canceled*. 
*Jan 9.* Statistical queries and learning parity with noise. 
*Jan 16.* Support vector machines and fourier methods. We will use slides by Martin Law. 
*Jan 23.* SVMs, Fourier, and Agnostic Learning. 
*Other models of learning*

*Jan 30.* Online optimization. See paper by Zinkevich. For extension to the bandit setting, see this paper. 
*Feb 6.* Learning in repeated games. See Regret in the On-line Decision Problem by Foster and Vohra for a great introduction. To see the specific reduction from external to internal regret I was talking about, see From External to Internal Regret by Avrim Blum and Yishay Mansour. يمكنكم استخدام الروابط مع التحية


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## حسن هادي (23 أبريل 2007)

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## حسن هادي (23 أبريل 2007)

http://www.sciencedirect.com/scienc...serid=10&md5=cc407dcac99bf8aeccb11fab2d4c43b7


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## حسن هادي (23 أبريل 2007)

*A Glossary for Molecular Information Theory and the Delila System *

by Tom Schneider and Karen Lewis 

http://www.ccrnp.ncifcrf.gov/~toms/glossary.html 


 Molecular Information Theory Glossary Terms (separate window) 
Molecular Information Theory Glossary Terms with a control frame on the left! 
Glossary without frames. 
Suggestions for new terms are welcome! 


*absolute coordinate*: A number (usually integer) that describes a specific position on a nucleic acid or protein sequence. An example of using two absolute coordinates in Delila instructions is: "*get from 1 to 6;*" The numerals *1* and *6* are absolute coordinates. See also: relative coordinate. 
*acceptor splice site*: The binding site of the spliceosome on the 3' side of an intron and the 5' side of an exon. This term is preferred over "3' site" because there can be multiple acceptor sites, in which case "3' site" is ambiguous. Also, one would have to refer to the 3' site on the 5' side of an exon, which is confusing. Mechanistically, an acceptor site defines the beginning of the exon, not the other way around. See 
Example 1: There are two acceptor sites at the 3' end of intron 3 of the iduronidase synthetase gene: the normal site (12.7 bit) and a strong (8.9 bit) cryptic acceptor site Calling both of these "3' sites" would be confusing.
Example 2: A mutation, G863A in the ABCR gene creates a 9.8 bit acceptor site 3 bases downstream from the normal site.
donor splice site.
sequence walker.
*acronymology*: The study of words (as radar, snafu) formed from the initial letter or letters of each of the successive parts or major parts of a compound term. See also: acronymology example. 
*administrivia*: [Pronunciation: combine administ[ration] and trivia. Function: noun. Etymology: coined by TD Schneider. Date: before 2000] administritrative trivia 
*after state (after sphere, after)*: the low energy state of a molecular machine after it has made a choice while dissipating energy. This corresponds to the state of a receiver in a communications system after it has selected a symbol from the incoming message while dissipating the energy of the message symbol. The state can be represented as a sphere in a high dimensional space. See also: Shannon sphere, gumball machine, channel capacity. 
*alignment (align)*: a set of binding site or protein sequences can be brought into register so that a biological feature of interest is emphasized. A good criterion for finding an alignment is to maximize the information ******* of the set. This can be done for nucleic acid sequences by using the malign program. See also: 
Maximimizing information by alignment.
zero coordinate
*before state (before sphere, before)*: the high energy state of a molecular machine before it makes a choice. This corresponds to the state of a receiver in a communications system before it has selected a symbol from the incoming message. The state can be represented as a sphere in a high dimensional space. See also: Shannon sphere, gumball machine, channel capacity. 
*binding site*: the place on a molecule that a recognizer (protein or macromolecular complex) binds. In this glossary, we will usually consider nucleic acid binding sites. A classic example is the set of binding sites for the bacteriophage Lambda Repressor (cI) protein on DNA (M. Ptashne, How eukaryotic transcriptional activators work, Nature, 335, 683-689, 1988). These happen to be the same as the binding sites for the Lambda cro protein. (The text mentioned in the figure is Sequence Logos: A Powerful Yet Simple, Tool.) See also 
binding site symmetry


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## حسن هادي (23 أبريل 2007)

http://www.lecb.ncifcrf.gov/~toms/glossary.html


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## حسن هادي (23 أبريل 2007)

تحياتي اخوكم حسن هادي


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