" This is a graduate course on the design and analysis of algorithms, covering several advanced topics not studied in typical introductory courses on algorithms. It is especially designed for doctoral students interested in theoretical computer science."

This course will present advanced topics in Artificial Intelligence (AI), including inquiries into logic, artificial neural network and machine learning, and the Turing machine. Upon successful completion of this course, students will be able to: define the term 'intelligent agent,' list major problems in AI, and identify the major approaches to AI; translate problems into graphs and encode the procedures that search the solutions with the graph data structures; explain the differences between various types of logic and basic statistical tools used in AI; list the different types of learning algorithms and explain why they are different; list the most common methods of statistical learning and classification and explain the basic differences between them; describe the components of Turing machine; name the most important propositions in the philosophy of AI; list the major issues pertaining to the creation of machine consciousness; design a reasonable software agent with java code. (Computer Science 408)

Following a brief classroom discussion of relevant principles, each student completes the paper design of several advanced circuits such as multiplexers, sample-and-holds, gain-controlled amplifiers, analog multipliers, digital-to-analog or analog-to-digital converters, and power amplifiers. One of each student's designs is presented to the class, and one may be built and evaluated. Associated laboratory emphasizing the use of modern analog building blocks. Alternate years.

This course introduces students to Advanced Computer Organization and Architecture. Where architecture is a term used to describe the attributes of a system as seen by the programmer. Its concerned with designs and operations of a computer. Computer organization is the way the system is structured so that all those cataloged tools can be used, and that in an efficient fashion. This course has its emphasis on system design and performance.

There are both practical and theoretical reasons to study algorithms. From a practical standpoint, you have to know a standard set of important algorithms from different areas of computing; in addition, you should be able to design new algorithms and analyze their efficiency. From the theoretical standpoint, the study of algorithms, sometimes called algorithmics, has come to be as the cornerstone of computer science.

At the heart of any major system is a data storage back-end. This back-end is what is refered to as a database. Taking that a database forms the core of systems, there is every need for the data to have integrity and also available for use. Database systems when well designed will ensure the system achieves those goals. Persons with skills to develop and manage these databases are therefore vital in modern day systems. The course principals of Database Systems is needed to help provide the competencies and skills needed by entry-level systems analyst or programmers. This course is about understanding and developing application logic in databases.

This course will expand upon SQL as well as other advanced topics, including query optimization, concurrency, data warehouses, object-oriented extensions, and XML. Additional topics covered in this course will help you become more proficient in writing queries and will expand your knowledge base so that you have a better understanding of the field. Upon successful completion of this course, the student will be able to: write complex queries, including full outer joins, self-joins, sub queries, and set theoretic queries; write stored procedures and triggers; apply the principles of query optimization to a database schema; explain the various types of locking mechanisms utilized within database management systems; explain the different types of database failures as well as the methods used to recover from these failures; design queries against a distributed database management system; perform queries against database designed with object-relational extensions; develop and query XML files. (Computer Science 410)

Recent results in cryptography and interactive proofs. Lectures by instructor, invited speakers, and students. Alternate years. The topics covered in this course include interactive proofs, zero-knowledge proofs, zero-knowledge proofs of knowledge, non-interactive zero-knowledge proofs, secure protocols, two-party secure computation, multiparty secure computation, and chosen-ciphertext security.

" This course covers concepts and techniques for the design and implementation of large software systems that can be adapted to uses not anticipated by the designer. Applications include compilers, computer-algebra systems, deductive systems, and some artificial intelligence applications. Topics include combinators, generic operations, pattern matching, pattern-directed invocation, rule systems, backtracking, dependencies, indeterminacy, memoization, constraint propagation, and incremental refinement. Substantial weekly programming Assignments and Labs are an integral part of the subject. There will be extensive programming Assignments and Labs, using MIT/GNU Scheme. Students should have significant programming experience in Scheme, Common Lisp, Haskell, CAML or some other "functional" language."

This book is a guide to implement simple agent-based evolutionary models using NetLogo.

All the models we implement are agent-based, i.e. individual agents and their interactions are explicitly represented in the models. To formalise agents’ interactions we use the basic framework of Evolutionary Game Theory.

NetLogo is a multi-agent programmable modeling environment used by hundreds of thousands of students, teachers and researchers all around the globe. No coding experience is necessary to fully understand the contents of this book.

Table of Contents
0. Introduction

0.1. Introduction to evolutionary game theory
0.2. Introduction to agent-based modeling
0.3. Introduction to Netlogo
0.4. The fundamentals of NetLogo
1. Our first agent-based evolutionary model

1.0. Our very first model
1.1. Extension to any number of strategies
1.2. Noise and initial conditions
1.3. Interactivity and efficiency
1.4. Analysis of these models
1.5. Answers to exercises
2. Spatial interactions on a grid

2.0. Spatial chaos in the Prisoner's Dilemma

Building on Complex Adaptive Systems theory and basic Agent Based Modeling knowledge presented in SPM4530, the Advanced course will focus on the model development process. The students are expected to conceptualize, develop and verify a model during the course, individually or in a group. The modeling tasks will be, as much as possible, based on real life research problems, formulated by various research groups from within and outside the faculty.
Study Goals The main goal of the course is to learn how to form a modeling question, perform a system decomposition, conceptualize and formalize the system elements, implement and verify the simulation and validate an Agent Based Model of a socio-technical system.

There are both practical and theoretical reasons to study algorithms. From a practical standpoint, you have to know a standard set of important algorithms from different areas of computing; in addition, you should be able to design new algorithms and analyze their efficiency. From the theoretical standpoint, the study of algorithms, sometimes called algorithmics, has come to be as the cornerstone of computer science.

This course aim to provide an in-depth understanding of the fundamental algorithmic techniques for design and analysis, in turn impart knowledge and practical competence in use of advanced data structures and the design and Welcome to Advanced computer Security Module. This module provides a study of high-level computer security issues in computer networks and advanced methods of data encryption. It focuses on advanced aspects of computer security, such as encryption, security practices, system security, security for authentication on the Web and password management techniques. Finally this module, students should be able to create secure network architectures adapted to the investment level and required security. Take responsibility for installation, configuration and network security maintenance.

The module aims to give IT infrastructure management skills, where the role of computer security is critical to ensure the integrity of data and the normal operation of the various systems: computer networks, servers and personal computers in the organization. The course will also explore various Information Security controls, how to handle various risk assessment in an organization and finally creating a security policy in organization.

Today, we note that the information is considered the key business of an organization / company due to its usefulness and importance, however, the issue of the company’s Information Security is a priority task for managers, because they recognize the value it has and therefore organizations must make sure that it is managed effectively. For this reason, this module is important because you will learn the methods and tools for computer security that can ensure the confidentiality of information in organizations and also learn how to protect their information and systems in a network environment.

There are both practical and theoretical reasons to study algorithms. From a practical standpoint, you have to know a standard set of important algorithms from different areas of computing; in addition, you should be able to design new algorithms and analyze their efficiency. From the theoretical standpoint, the study of algorithms, sometimes called algorithmics, has come to be as the cornerstone of computer science.

This course aim to provide an in-depth understanding of the fundamental algorithmic techniques for design and analysis, in turn impart knowledge and practical competence in use of advanced data structures and the design and analysis of algorithms.

This course focuses on the fundamentals of computer algorithms, emphasizing methods useful in practice. Upon successful completion of this course, the student will be able to: explain and identify the importance of algorithms in modern computing systems and their place as a technology in the computing industry; indentify algorithms as a pseudo-code to solve some common problems; describe asymptotic notations for bounding algorithm running times from above and below; explain methods for solving recurrences useful in describing running times of recursive algorithms; explain the use of Master Theorem in describing running times of recursive algorithms; describe the divide-and-conquer recursive technique for solving a class of problems; describe sorting algorithms and their runtime complexity analysis; describe the dynamic programming technique for solving a class of problems; describe greedy algorithms and their applications; describe concepts in graph theory, graph-based algorithms, and their analysis; describe tree-based algorithms and their analysis; explain the classification of difficult computer science problems as belonging to P, NP, and NP-hard classes. (Computer Science 303)

This is a textbook for first year Computer Science. Algorithms and Data Structures With Applications to Graphics and Geometry.

In-depth study of an active research topic in computer graphics. Topics change each term. Readings from the literature, student presentations, short assignments, and a programming project. Animation is a compelling and effective form of expression; it engages viewers and makes difficult concepts easier to grasp. Today's animation industry creates films, special effects, and games with stunning visual detail and quality. This graduate class will investigate the algorithms that make these animations possible: keyframing, inverse kinematics, physical simulation, optimization, optimal control, motion capture, and data-driven methods. Our study will also reveal the shortcomings of these sophisticated tools. The students will propose improvements and explore new methods for computer animation in semester-long research projects. The course should appeal to both students with general interest in computer graphics and students interested in new applications of machine learning, robotics, biomechanics, physics, applied mathematics and scientific computing.

This course will provide an overview of a new vision for Human-Computer Interaction (HCI) in which people are surrounded by intelligent and intuitive interfaces embedded in the everyday objects around them. It will focus on understanding enabling technologies and studying applications and experiments, and, to a lesser extent, it will address the socio-cultural impact. Students will read and discuss the most relevant articles in related areas: smart environments, smart networked objects, augmented and mixed realities, ubiquitous computing, pervasive computing, tangible computing, intelligent interfaces and wearable computing. Finally, they will be asked to come up with new ideas and start innovative projects in this area.

Este libro está dirigido, principalmente, a Estudiantes y Docentes que quieren aprender a programar como forma de fortalecer sus capacidades cognoscitivas y así obtener un beneficio adicional de su computador para lograr un mejor provecho de sus estudios. Dada la orientación del libro respecto a programar para resolver problemas asociados a las Ciencias e Ingenierías, el requisito mínimo de matemáticas que hemos elegido para presentar el contenido del mismo se cubre, normalmente, en el tercer año del bachillerato. No obstante, el requisito no es obligatorio para leer el libro en su totalidad y adquirir los conocimientos de programación obviando el contenido matemático.

Artificial Intelligence is a subject is one of the most important and exciting sub-fields of Computer Science. This specialist programme covers the fundamentals of Computer Science and Computer Information Systems so as to maximise your future employment opportunities. It also offers the chance to carry out cutting-edge research in this high profile area.

Introductory modules cover Programming in Java, Computer Systems, Databases, Human-Centric Computing, and Algorithmic Foundations. The course is completed by a selection of modules covering important topics within the subject: Knowledge Representation, the study of Multi-Agent Systems, and Robotics among the others. The course also includes a related second year group project and an individual year project.

This course includes materials on AI programming, logic, search, game playing, machine learning, natural language understanding, and robotics, which will introduce the student to AI methods, tools, and techniques, their application to computational problems, and their contribution to understanding intelligence. The material is introductory; the readings cite many resources outside those assigned in this course, and students are encouraged to explore these resources to pursue topics of interest. Upon successful completion of this course, the student will be able to: Describe the major applications, topics, and research areas of artificial intelligence (AI), including search, machine learning, knowledge representation and inference, natural language processing, vision, and robotics; Apply basic techniques of AI in computational solutions to problems; Discuss the role of AI research areas in growing the understanding of human intelligence; Identify the boundaries of the capabilities of current AI systems. (Computer Science 405)