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Cs 6601 gatech github

By | 05.10.2020

This is an interactive hands-on course that will teach students the principles of design at the individual level.

cs 6601 gatech github

Introduction to Information Security. A broad spectrum of information security: threats, basic cryptography, software vulnerabilities, programming for malice, operating system protections, network security, privacy, data mining, computer crime.

Exploring challenges faced by underserved populations and developing countries from a computing perspective.

Computer Science (CS)

Graduate Introduction to Operating Systems. Introduction to graduate-level topics in operating systems using research papers, textbook excerpts, and projects. Provides students thorough comprehension of distributed and parallel computer systems. Big Data Systems and Analytics.

Game Artificial Intelligence

This course will cover the concepts, techniques, algorithms, and systems of big data systems and data analytics, with strong emphasis on big data processing systems, fundamental models and opotimizations for data analytics and machine learning, which are widely deployed in real world big data analytics and applications.

Introduction to MIMD parallel computation, using textbook excerpts, research papers, and projects on multiple parallel machines. Emphasizes practical issues in high-performance computing.

Real-Time System Concepts and Implementation. Principles of real-time systems, as occurring in robotics and manufacturing, interactive, and multimedia applications. Reviews and uses real-time operating systems. Design principles of secure systems, authentication, access control and authorization, discretionary and mandatory security policies, secure kernel design, and secure databases. Design and Implementation of Compilers.

Design and implementation of modern compilers, focusing upon optimization and code generation. Design and implementation of compilers for parallel and distributed computers, focusing upon optimization and code generation. Object-Oriented Systems and Languages. Design and implementation of object-oriented systems.Skip to content. Students should be familiar with college-level mathematical concepts calculus, analytic geometry, linear algebra, and probability and computer science concepts algorithms, O notation, data structures.

cs 6601 gatech github

In addition to this, students should have working knowledge of computer programming; the course will focus on using Python for its programming assignments. This course counts towards the following specialization s : Computational Perception and Robotics Interactive Intelligence.

Spring syllabus Fall syllabus and schedule PDF. Note: Sample syllabi are provided for informational purposes only.

For the most up-to-date information, consult the official course documentation. You should have completed undergraduate computer algorithm and data structures courses that cover O notation, time and space constraints. You should have working knowledge of college level mathematics such as calculus, probability, and linear algebra.

You will also need to be familiar with Python and be comfortable making modifications to large programs. Your system must be able to install the latest release of Python 3.

Please check the official documentation for more information. This course may impose additional academic integrity stipulations; consult the official course documentation for more information.

CS Artificial Intelligence. Instructional Team Thad Starner Creator, Instructor Thomas Ploetz Instructor Maksim Sorokin Head TA Overview Students should be familiar with college-level mathematical concepts calculus, analytic geometry, linear algebra, and probability and computer science concepts algorithms, O notation, data structures. This course counts towards the following specialization s : Computational Perception and Robotics Interactive Intelligence Sample Syllabi Spring syllabus Fall syllabus and schedule PDF Note: Sample syllabi are provided for informational purposes only.

Before Taking This Class Suggested Background Knowledge You should have completed undergraduate computer algorithm and data structures courses that cover O notation, time and space constraints. If not, are you comfortable in learning a language within the first week of class?

Have you taken several classes that required intensive programming? Have you taken algorithms and data structures courses?

Are you prepared to spend at least 9 hours a week on this class? Technical Requirements and Software Your system must be able to install the latest release of Python 3. Williams Paper Museum. Thad Starner Creator, Instructor. Thomas Ploetz Instructor. Maksim Sorokin Head TA.Instructor: Kaushik Subramanian, ksubrama cc.

cs 6601 gatech github

TA: Karl Gemayel, karl gatech. Machine Learning ML is that area of Artificial Intelligence that is concerned with computational artifacts that modify and improve their performance through experience.

The area is concerned with issues both theoretical and practical. We will cover a variety of topics, including: statistical supervised and unsupervised learning methods, randomized search algorithms, Bayesian learning methods, and reinforcement learning. The course also covers theoretical concepts such as inductive bias, the PAC and Mistake-bound learning frameworks, minimum description length principle, and Ockham's Razor. In order to ground these methods the course includes some programming and involvement in a number of projects.

Almost all ML applications today require you to think like a researcher. The last objective will be at the core of this course. The official prerequisite for this course is an introductory course in artificial intelligence. Apart from this, the most important prerequisite for enjoying and doing well in this class is your interest in the material. If you are not sure whether this class is for you, please talk to me.

We will also use supplemental readings as well, but those will be provided for you. Weblinks to ML toolboxes and datasets will provided for you in the Resources tab.

Also you are free to use whatever machines you want to do your work; however, the final result will have to run on the standard CoC boxes.

We will use the Tsquare page to post course announcements, so check it early and often. We will also use Piazza to discuss course material offline. Course schedule is available in the Resources tab. Your final grade is divided into four components: assignments, a group project, a midterm exam and a final exam. There will be two graded assignments. They will be about programming and analysis. Generally, they are designed to give you deeper insight into the material and to prepare you for the exams.

The programming will be in service of allowing you to run and discuss experiments, do analysis, and so on. Group Project. There is a semester-long group project. You will use it to pursue a topic of your interest in Machine Learning. At the end of the term, you will be required to produce a NIPS-style conference paper, and to give a short presentation. Along the way, your group will turn in a very short proposal and a somewhat longer progress report.

The guidelines for the group project are provided in the Resources tab. Final Exam. There will be a written, closed-book final exam at the time that has been scheduled for our class' final exam. Although class participation is not explictly graded, I will use your class participation to determine whether your grade can be lifted in case you are right on the edge of two grades. Participation means attending classes, participating in class discussions, asking relevant questions, volunteering to provide answers to questions, and providing constructive criticism and creative suggestions that improve the course.Instructor: Mark Riedl, riedl cc.

Teaching Assistant: Matthew Guzdial, mguzdial42 gatech. Teaching Assistant: Sebastian Monroy, sebash gatech. The purpose of this course is for undergraduates and graduates in Computer Science and Computational Media to gain a breadth of understanding in the toolbox of AI approaches employed in digital games. This involves learning some basic topics covered in other AI courses, but with a focus on applied knowledge within the context of digital games.

Game AI is distinct from "academic AI" in that the end behavior is the target. Game AI programmers are less concerned with the underlying algorithms and more so with the end result. There are also characteristics of many games that focus Game AI on specific problems, like navigation through a virtual world, tactics, and believable behavior.

Academic AI researchers are more concerned with rational behavior, knowledge representations, robust multi-agent communication, etc. However, there are overlaps between the two domains, where the desired behavior requires less cheating and more realistic decision-making.

This course will survey topics related to this overlap, with a focus on applying what we review in depth through implementations in digital games. This course also observes the difference between AI as a technical challenge for opposing forces AI in games and the integration of AI as a key aesthetic component of the gaming experience. Lectures and projects will explores both of these views of Game AI. This syllabus should be considered a living document subject to change throughout the course of the semester.

There are multiple places in the class schedule to accommodate student interests in particular subjects. Students should have taken a previous course on artificial intelligence.

For graduate students this may be CS or any equivalent course from their undergrad institution. Students are required to have solid programming skills. Experience with Java or the ability to pick it up as part of the course is required. Students are expected to be able to pick up pre-existing code bases and develop their AI code within that code base as part of the class. Lectures: The course will be conducted through lectures and occasional participatory exercises.

The instructor may call on students to answer questions about the reading during lecture. The instructor may give occasional pop quizzes. Homeworks: Students will demonstrate their proficiency at Game AI material through a sequence of short-sprint week homework assignments. The sequence of homework assignments together comprises a full-scale project in which all aspects of the AI for complete game are implemented.

Because homework assignments build on each other, the instructor will provide sample solutions after each sprint. Final Project: Students will engage in a self-directed final project in which game levels will be algorithmically generated.Disclaimer: This syllabus and webpage is a mashup of the Game AI syllabi written by previous instructors of the course held at Georgia Tech.

Mark RiedlDr. Brian Magerkoand Dr. Brian O'Neill. The purpose of this course is for undergraduate and graduate students in Computing and related fields to gain a breadth of understanding of the toolbox of AI approaches employed in digital games. This involves learning some basic topics covered in other AI courses, but with a focus on applied knowledge within the context of digital games.

The discipline of academic Game AI was launched with a justification of interactive entertainment i. There is an additional industry perspective on AI for games: increasing the engagement and enjoyment of the player.

This perspective is consistent with the perspective of computer game developers. For them, AI is a tool in the arsenal of the game to be used in lieu of real people when no one is available for a given role.

Lectures and projects will explores both of these views of Game AI, with an emphasis on the industry perspective. This course also observes the difference between AI as a technical challenge for opposing forces AI in games and the integration of AI as a key aesthetic component of the gaming experience. Lectures and projects will explores both of these views of Game AI. Game AI programmers are less concerned with the underlying algorithms and more so with the end result.

There are also characteristics of many games that focus Game AI on specific problems, like navigation through a virtual world, tactics, and believable behavior. Academic AI researchers are more concerned with rational behavior, knowledge representations, robust multi-agent communication, etc. However, there are overlaps between the two domains, where the desired behavior requires less cheating and more realistic decision-making.

This course will survey topics related to this overlap, with a focus on applying what we review in depth through implementations in digital games. Students in this course will: LO We will use Piazza as our main method of electronic communications and announcements. All students should join the "CS " course. Students will be responsible for any announcements made there.

The use of the group is a resource for technical and design issues. For assignment submissions, we will use T-square. As a means of avoiding confusion about where to look for information, we will primarily use Piazza for all communication this summer. Rather than emailing technical questions to me, I encourage you to post your questions on Piazza to foster community. Failure to include this in the subject may result in misfiling of the email and a lack of response.

Assignments are due at pm via T-Square on the announced due date. Late work will not be accepted under any circumstances. Students will demonstrate their proficiency at Game AI material through a sequence of short-sprint week homework assignments and a self-directed capstone project.

The sequence of homework assignments together comprises a full-scale project in which all aspects of the AI for a complete game are implemented. Failure to use backup and version control development practices is not a valid excuse for late or missing submissions.

I reserve the right to give unannounced quizzes as a means to get us back on track if attendance, participation, or completion of reading assignments fall below my expectations. There may be one or two optional homework assignments for extra credit toward one's final course grade. At least one of the extra credit projects will be to participate in a bot-versus-bot competition.GitHub is home to over 40 million developers working together to host and review code, manage projects, and build software together.

In the above paper, you will get a chance to generalize minimax search techniques to games with more than three players. As you'll see, alpha-beta pruning doesn't work quite as effectively in this case as in the general case.

Here are a few questions to keep in mind while reading through this paper:. Skip to content. Permalink Dismiss Join GitHub today GitHub is home to over 40 million developers working together to host and review code, manage projects, and build software together. Sign up. Branch: master. Find file Copy path. Cannot retrieve contributors at this time. Raw Blame History. Here are a few questions to keep in mind while reading through this paper: Why might alphabeta pruning only work well in the two player case?

How does the size of the search tree change with more than two players? You signed in with another tab or window. Reload to refresh your session. You signed out in another tab or window.A Graduate Course in Artificial Intelligence.

Professor: Thad Starner. TA: Ian Stewart gatech. TA: Stefano Fenu gatech. This is a graduate class. Having taken an AI class before will definitely make the class easier, but motivated students will be able to survive by self-study of the foundational material, which I will not lecture on in detail.

Rather, you will be asked to review or self-study the basic material prior to each module see below. All communication from me will be done through T-square. Please read all announcements and email promptly. The desired learning outcomes for the students are:. There are several activities designed to achieve the learning outcomes above:. While many detailed algorithms and processes can always be referenced in a textbook, being able to reason from principles on-the-fly is critical for discussions with colleagues.

More details will be communicated at appropriate times throughout the course, including grading criteria and standards.

Structure and Sequence of Class Activities. This course is probably different from many other courses you have taken at Georgia Tech, in that it does not follow the usual lecture pattern. Instead, while there are also conventional lectures, the course is different in two major aspects:. You will be asked to re- read the chapters in AIMA before the start of each module, as indicated on the schedule.

Reading textbook material can be tedious, but it is necessary for you to acquire this foundation if you have not previously taken an AI class, or review it if you did. To motivate you and at the same time reward you with a grade for your hard work, an assignment based on this reading material is due the day we start with the in-depth discussions needing those foundational chapters. A detailed schedule, subject to change, can be found on the schedule page.

I will be using the blackboard a lot, rather than powerpoint. Students are expected to take notes and consult the primary sources on the material, available from the website. Collaboration on assignments is encouraged at the "white board interaction" level. That is, share ideas and technical conversation, but write your own code. Students are expected to abide by the Georgia Tech Honor Code. Honest and ethical behavior is expected at all times.

All incidents of suspected dishonesty will be reported to and handled by the office of student affairs. The top N-1 of the N assignments will be counted in the final grade. Class will always begin with a question or two based on the readings and on-line videos. However, these will not be graded. As there is a lot of material to cover, "challenge questions" at the beginning of each section will demonstrate my focus for each unit and preview the skills I expect the student to learn in their reading.

There will be a midterm and final held in class as well. Communication about the class: All communication from me will be done through T-square. Out-of-class Work There are several activities designed to achieve the learning outcomes above: 1 Foundation, Skills, and Integration: there will be assignments on foundational material, due at the beginning of each module see below.

Structure and Sequence of Class Activities This course is probably different from many other courses you have taken at Georgia Tech, in that it does not follow the usual lecture pattern. Instead, while there are also conventional lectures, the course is different in two major aspects: 1 You are expected to review or study the foundational material outside class time. Schedule A detailed schedule, subject to change, can be found on the schedule page.

Materials I will be using the blackboard a lot, rather than powerpoint.


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