סילבוס הקורס נושאים נבחרים בהנדסת תעשייה: כלים הסתברותיים מתק' ושימושיהם

שנה"ל תשע"ה

נושאים נבחרים בהנדסת תעשייה: כלים הסתברותיים מתק' ושימושיהם
Selected Topics in Industrial Eng:Advanced Probabilistic Too

0572-5350-01

הנדסה | תואר שני - הנדסת תעשייה


סמ' א'	1700-1900	'ב	106	הנדסת תוכנה	שיעור	פרופ גרציאק יגאל

D:\Inetpub\shared\yedion\syllabus\05\2014\0572\0572535001_desc.txt סילבוס מקוצר דרישות קדם: - חשבון דיפרנציאלי מרובה-משתנים. - מודלים סטוכסטיים.- מושגים בסיסיים של תורת המלאי. 1. פילוגים חד-ממדיים,בעית מוכר עיתונים והרחבות, סטטיסטי סדר ויישומיהם, Pooling (איחוד מלאים). 2. סידורים סטוכסטיים ויישומיהם. 3. פילוגים רב-ממדיים, סוגי תלות, חסמים, Copulas. יישומים להערכת סיכונים וקבלת החלטות. 4. הקצאת עלויות/רווחים בשרשראות אספקה אופקיות. 5. (אם יתאפשר) ניהול פדיון ונושאים קשורים. Course description Lecture: 2 hours Overview: foundations, scope, problems, and approaches of AI. From brain models of technology: perceptron, feedforward neural networks, recurrent neural networks, neural models of memory, neural models of clustering, learning methods with neural networks, practical applications with artificial neural networks. From neurons to Intelligent agents: reactive, deliberative, goal-driven, utility-driven, and learning agents Problem-solving through Search: forward and backward, state-space, blind, heuristic, problemreduction, A, A, AO, minimax, constraint propagation, neural, stochastic, and evolutionary search algorithms, sample applications. Knowledge Representation and Reasoning: ontologies, foundations of knowledge representation and reasoning, representing and reasoning about objects, relations, events, actions, time, and space; predicate logic, situation calculus, description logics, reasoning with defaults, reasoning about knowledge, sample applications. Planning: planning as search, partial order planning, construction and use of planning graphs Representing and Reasoning with Uncertain Knowledge: probability, connection to logic, independence, Bayes rule, bayesian networks, probabilistic inference, sample applications. The following will be fast learnt in class and groups will take topics with projects: Machine Learning and Knowledge Acquisition: classifiers, clustering techniques. Include based on time permitting exploration, nearest neighbor, decision tree classifiers, Q-learning, multi-agent systems, support vector machines, support vector clustering. Sample Applications of AI, student project presentations. The text for the course are: 1. Artificial Intelligence: A Modern Approach, 3rd Edition, by Stuart Russell and Peter Norvig., 2. Neural Networks: A Comprehensive Foundation (2nd Edition) by Simon Haykin OR Neural Networks and Learning Machines (3rd Edition) by Simon Haykin The course will often draw upon a variety of additional readings to supplement the treatment of topics available in the primary textbook.

ש"ס: 2.0

סילבוס מקוצר

דרישות קדם: - חשבון דיפרנציאלי מרובה-משתנים.

- מודלים סטוכסטיים.- מושגים בסיסיים של תורת המלאי.

1. פילוגים חד-ממדיים,בעית מוכר עיתונים והרחבות, סטטיסטי סדר ויישומיהם, Pooling (איחוד מלאים).

2. סידורים סטוכסטיים ויישומיהם.

3. פילוגים רב-ממדיים, סוגי תלות, חסמים, Copulas. יישומים להערכת סיכונים וקבלת החלטות.

4. הקצאת עלויות/רווחים בשרשראות אספקה אופקיות.

5. (אם יתאפשר) ניהול פדיון ונושאים קשורים.

Course description

Lecture: 2 hours

Overview: foundations, scope, problems, and approaches of AI.

From brain models of technology: perceptron, feedforward neural networks, recurrent neural

networks, neural models of memory, neural models of clustering, learning methods with neural

networks, practical applications with artificial neural networks.

From neurons to Intelligent agents: reactive, deliberative, goal-driven, utility-driven, and

learning agents

Problem-solving through Search: forward and backward, state-space, blind, heuristic, problemreduction,

A, A*, AO*, minimax, constraint propagation, neural, stochastic, and evolutionary

search algorithms, sample applications.

Knowledge Representation and Reasoning: ontologies, foundations of knowledge

representation and reasoning, representing and reasoning about objects, relations, events, actions,

time, and space; predicate logic, situation calculus, description logics, reasoning with defaults,

reasoning about knowledge, sample applications.

Planning: planning as search, partial order planning, construction and use of planning graphs

Representing and Reasoning with Uncertain Knowledge: probability, connection to logic,

independence, Bayes rule, bayesian networks, probabilistic inference, sample applications.

The following will be fast learnt in class and groups will take topics with projects:

Machine Learning and Knowledge Acquisition: classifiers, clustering techniques. Include

based on time permitting exploration, nearest neighbor, decision tree classifiers, Q-learning,

multi-agent systems, support vector machines, support vector clustering.

Sample Applications of AI, student project presentations.

The text for the course are:

1. Artificial Intelligence: A Modern Approach, 3rd Edition, by Stuart Russell and Peter Norvig.,

2. Neural Networks: A Comprehensive Foundation (2nd Edition) by Simon Haykin OR Neural

Networks and Learning Machines (3rd Edition) by Simon Haykin

The course will often draw upon a variety of additional readings to supplement the treatment of

topics available in the primary textbook.

להצהרת הנגישות