微信客服：xiaoxionga100
微信客服：ITCS521
CIT5210-CIS5210-Artificial Intelligence代写
时间：2024-01-18
CIT 5210 - Artificial Intelligence | Spring 2024 v1 | Property of Penn Engineering
CIS 5210 - Artificial Intelligence
Spring 2024
Instructor
Harry Smith
Course Description
This course provides a broad introduction to the field of artificial intelligence. The focus of the course
is on developing algorithms for agents which sense and act in the world, so that they can make rational
decisions. Topics include, search and shortest paths algorithms, knowledge representation, probabilistic
reasoning, machine learning and neural networks, and natural language processing. All programming
assignments will be in Python, and we have a quick review of the programming language at the start
of this course.
Course Learning Objectives
● The aim of this course is to give you a broad overview of Artificial Intelligence.
● In this course we’ll focus primarily on building algorithms that can be used for constructing
AI agents that act rationally including search algorithms, constraint satisfaction problems,
Markov Decision Processes, reinforcement learning, machine learning algorithms and
Natural Language Processing.
● After the course, you will be able to apply AI algorithms in your own project and work.
Course Prerequisites
● CIT 5910 (Introduction to Software Development)
● CIT 5920 (Mathematical Foundations of Computer Science)
● CIT 5940 (Data Structures & Software Design)
● CIT 5960 (Algorithms and Computation)
Course Materials
Course Textbook
Required: Artificial Intelligence: A Modern Approach (4th edition) by Russel and Norvig. Note that
the 4th edition adds substantial new material over the 3rd edition, so you should buy the 4th edition.
CIT 5210 - Artificial Intelligence | Spring 2024 v1 | Property of Penn Engineering
You can buy the textbook on Amazon or from the UPenn bookstore, where you can rent the digital
version for $40.
Supplemental: Speech and Language Processing (3rd ed. draft) by Jurafsky and Martin. This textbook
is currently free online while the textbook authors are revising it to write their 3rd edition. We will
use it in the last third of the course.
Grading & Assessment
You must attempt all graded assignments to pass the course. If you have any questions or concerns
about grading or progress in the course, please reach out to your instructor.
This course will use a variety of assessments to determine whether learners understand and can apply
the key concepts and skills that the course teaches. This includes:
Type % Description
Individual Coding
assignments
60% There are 10 individual coding assignments in this course that will
test core concepts. They are all equally weighted and will all be
administered via Gradescope.
All coding assignments will be autograded.
Exams 30% There are 2 midterm exams, which are equally weighted. They
will both be timed exams.
Quizzes 10% This course has 13 quizzes, which are all autograded in Canvas.
These will be equally weighted.
Please read the instructions for each assignment very carefully to make sure you know where to
submit to receive credit!
This course is not curved. Your overall score is computed as 0.3 * total exam score + 0.6 * total
homework score + 0.1 * total quiz score. Here is how we assign letter grades based on your overall
score:
Score Grade
>= 98 A+
93-98 A
90-93 A-
CIT 5210 - Artificial Intelligence | Spring 2024 v1 | Property of Penn Engineering
87-90 B+
83-87 B
80-83 B-
75-80 C+
70-75 C
65-70 C-
50-65 D
below 50 F
Late Policy
If students submit their assignments later than the stated deadline, they will face a 10% penalty per
late day, till their score hits zero. For extraordinarily extenuating circumstances, please fill
out the extension request form located under the Course Resources page in Canvas.
Please fill out the form for any and all extension requests you wish to submit. These
extension requests must be submitted at least 24 hours before the assignment
deadline. If your request is granted, you will see updated deadlines reflected in
Canvas and Gradescope by Mondays at 5 pm ET. You will need to fill out the form for
each assignment you wish to have an extension for.
Regrade Requests
Regrade requests are allowed up to 1 week after the grades are released. These requests must be made
through Gradescope.
We welcome and encourage regrade requests, however, we kindly ask that you review your
work thoroughly before submitting a request. Students have access to three unsuccessful
requests, subsequent unsuccessful requests will result in a 1% deduction from your
assignment grade. Thank you for your understanding and cooperation.
Other Course Activities
The following activities are not mandatory but will greatly support your success on the graded
assignments.
Discussion Forum
Discussion forums are designed to give you optional extra practice with the material, and to see
examples of how your classmates are thinking and working.
CIT 5210 - Artificial Intelligence | Spring 2024 v1 | Property of Penn Engineering
Additional Segments
The professor may add additional optional segments to support the class as needed, including recitation
materials.
Getting and Giving Help
TA and Faculty Support
TAs will hold office hours weekly where they will open a queue in PennLab’s OHQ.io system Canvas
- OHQ.io Resource. Your instructor will hold a weekly Open Office Hour session. He will be
available for a limited number of private meetings per week, depending on the needs of the class.
Collaboration Guidelines
In the professional world of software development, collaboration—including using code that others
have written—is both practical and ubiquitous.
However, to prepare for entering that professional context, you need to develop a full set of software
development skills so that you are both able to create your own code and evaluate the quality of
someone else’s code that you might use.
In the context of this course, independent work and evaluation are critical. Do not collaborate with
others on individual graded assignments unless it is explicitly indicated. The inappropriate
collaboration will be considered cheating and considered under Penn’s Code of Academic Integrity.
Unless otherwise noted, you are not allowed to work in groups on the homework assignments. You
can discuss homework problems with others (you must explicitly list who you discussed problems with
on each homework submission), but all code must be your own independent work. You are not
allowed to upload your code to publicly accessible places (like public github repositories), and you are
not allowed to access anyone else’s code. If you discover someone else’s code online, please report it to
the course staff via a private note on Ed Discussion.
Discussion forums and recitations are collaborative—please take advantage of those times to work with
your colleagues. For general communication with your colleagues, use your Slack channels or Slack
direct messages.
Forming study groups to understand the material is also a good idea, as long as you stay on the
conceptual level and are not collaborating on the graded assignments directly.
CIT 5210 - Artificial Intelligence | Spring 2024 v1 | Property of Penn Engineering
Note: When in doubt always ask the instructor or TA first, to avoid any potential collaboration that
can lead to academic dishonesty.
Do not cheat. Please note that searching for solutions online is the same as cheating. Posting solutions
online is also considered cheating. If you are caught posting solutions or code to a publicly accessible
location (like StackOverflow or GitHub), it will be considered cheating. If you do use GitHub (or
similar cloud-based code management system) to set up a remote code repository, YOU ARE
REQUIRED TO KEEP THAT REPOSITORY PRIVATE.
You can further read Penn's Code of Academic Integrity page on this subject matter, as well as the
SEAS Graduate Student guidelines on the code of ethics.
Plagiarism Policy
The first instance of homework plagiarism will be handled by the instructor and may include escalation
to the Center for Community Standards and Accountability.
Second instances or exam plagiarism will be turned over immediately to the University of Pennsylvania
Center for Community Standards and Accountability.
Regardless of previous work in the course, the penalty for plagiarism is the failure of the course
(regardless of current course average), and potential permanent notation on your academic record that
will follow you to all future academic institutions and possibly future employers. If you are unfamiliar
with what constitutes plagiarism at Penn, visit Penn’s Code of Academic Integrity.
Please note that searching for solutions or code online is a violation of academic
integrity. Sharing solutions or code with another student (unless working on a group
project or other collaborative assignment) is also a violation of academic integrity.
This includes posting solutions and code publicly online, even after you've
completed the course. If you discover publicly viewable solutions for the
assignments of this course, please let the course staff know immediately. Ignorance
of this policy is not an excuse for failing to abide by it.
Guidelines for the Use of Generative AI in this Course
We recognize the increasing prevalence and power of Artificial Intelligence (AI) tools, and
encourage their responsible and ethical use to enhance your learning experience. However, it's
essential to develop a strong understanding of fundamental processes before relying on AI. To
that end:
CIT 5210 - Artificial Intelligence | Spring 2024 v1 | Property of Penn Engineering
Examples of acceptable uses of AI
• Comprehension and Expansion: You are encouraged to use AI to clarify and expand
your understanding of course materials.
• Research and Information Gathering: AI can supplement your research and information
gathering, aiding your exploration of complex concepts.
Examples of unacceptable uses of AI
• Assignment Completion: Avoid using AI for generating code or text for assignments,
peer reviews, or content summaries.
• Assessment Assistance: Do not seek AI help for quizzes, exams, or assessments.
• Academic Integrity: Do not present AI-generated content without citation and as your
own work.
Engaging in unacceptable use of AI tools will result in academic consequences, which may
include grade penalties, academic warnings, or other actions as determined by your instructor
and the university’s academic integrity policies.
Note that these guidelines may differ from those in other courses. If you have questions or
concerns, don't hesitate to reach out. Ultimately, AI can be a valuable educational tool when
used responsibly and aligned with our course policies.
Access to Materials and Content Before and After Graduation
If you would like to retain copies of your submitted assignments for personal use (please
do not make them public), you must download them from Gradescope, Canvas, Codio, and
any other platforms that you submit to during the semester in which you are taking that
course.
Access to course materials and your submissions is not guaranteed after the completion of
a course. Therefore, we recommend that students download any assignments or materials
they would like to keep before a course concludes.
Recording Notice
Public office hours, recitations, and other live events will be recorded, used, and may be made
available to class participants during the current semester as well as students who take the
class in future semesters.
Private office hours will also be offered and are not recorded. Students who do not wish to
attend the publicly-recorded office hour may attend the private office hours.
CIT 5210 - Artificial Intelligence | Spring 2024 v1 | Property of Penn Engineering
Spring 2024 Course Schedule and Important Dates
All dates are subject to change. Please check Ed Discussion for announcements regarding schedule
changes. Also, please check the Canvas Calendar for the most up-to-date information on due dates
etc.
Important to note* Homework assignments are due on Mondays. Quizzes are due on Sundays.
Tuesday, 1/16 -
Monday, 1/22
Module 1 Assignment(s)
Agent Quiz Due, Python Skills Quiz Due
Homework 1 Due
Tuesday, 1/23 -
Monday, 1/29
Module 2 Assignment(s)
Uninformed Search Quiz Due
Homework 2 Due
Tuesday 1/30 -
Monday 2/5
Module 3 Assignment(s)
Informed Search Quiz Due
Homework 3 Due
Tuesday 2/6 -
Monday 2/12
Module 4 Assignment(s)
Game and Adversarial Search Quiz Due
Homework 4 Due
Tuesday 2/13 -
Monday 2/19
Module 5 Assignment(s)
Constraint Satisfaction Problem Quiz Due
Homework 5 Due
Tuesday 2/20 -
Monday 2/26
Module 6 Assignment(s)
Logical Agents Quiz Due
Midterm Exam Due
Tuesday 2/27 -
Monday 3/4
Module 7 Assignment(s)
Markov Decision Processes Quiz Due
Homework 6 Due
Tuesday 3/11 -
Monday 3/18
Module 8 Assignment(s)
Reinforcement Learning Quiz Due
Homework 7 Due
CIT 5210 - Artificial Intelligence | Spring 2024 v1 | Property of Penn Engineering
Tuesday 3/19 -
Monday 3/25
Module 9 Assignment(s)
Probabilities and Language Models Quiz Due
Homework 8 Due
Tuesday 3/26 -
Monday 4/1
Module 10 Assignment(s)
Probabilistic Reasoning and Bayes’ Net Quiz Due
Tuesday 4/2 -
Monday 4/8
Module 11 Assignment(s)
Naïve Bayes and Perceptrons Quiz Due
Homework 9 Due
Tuesday 4/9 -
Monday 4/15
Module 12 Assignment(s)
Neural Net Quiz Due
Tuesday 4/16 -
Monday 4/22
Module 13 Assignment(s)
Natural Language Processing Quiz Due
Homework 10 Due
Tuesday 4/23 –
Sunday 4/28
Module 14 Assignment(s)
End of Term Exam Due
CIS 5210 - Artificial Intelligence
Spring 2024
Instructor
Harry Smith
Course Description
This course provides a broad introduction to the field of artificial intelligence. The focus of the course
is on developing algorithms for agents which sense and act in the world, so that they can make rational
decisions. Topics include, search and shortest paths algorithms, knowledge representation, probabilistic
reasoning, machine learning and neural networks, and natural language processing. All programming
assignments will be in Python, and we have a quick review of the programming language at the start
of this course.
Course Learning Objectives
● The aim of this course is to give you a broad overview of Artificial Intelligence.
● In this course we’ll focus primarily on building algorithms that can be used for constructing
AI agents that act rationally including search algorithms, constraint satisfaction problems,
Markov Decision Processes, reinforcement learning, machine learning algorithms and
Natural Language Processing.
● After the course, you will be able to apply AI algorithms in your own project and work.
Course Prerequisites
● CIT 5910 (Introduction to Software Development)
● CIT 5920 (Mathematical Foundations of Computer Science)
● CIT 5940 (Data Structures & Software Design)
● CIT 5960 (Algorithms and Computation)
Course Materials
Course Textbook
Required: Artificial Intelligence: A Modern Approach (4th edition) by Russel and Norvig. Note that
the 4th edition adds substantial new material over the 3rd edition, so you should buy the 4th edition.
CIT 5210 - Artificial Intelligence | Spring 2024 v1 | Property of Penn Engineering
You can buy the textbook on Amazon or from the UPenn bookstore, where you can rent the digital
version for $40.
Supplemental: Speech and Language Processing (3rd ed. draft) by Jurafsky and Martin. This textbook
is currently free online while the textbook authors are revising it to write their 3rd edition. We will
use it in the last third of the course.
Grading & Assessment
You must attempt all graded assignments to pass the course. If you have any questions or concerns
about grading or progress in the course, please reach out to your instructor.
This course will use a variety of assessments to determine whether learners understand and can apply
the key concepts and skills that the course teaches. This includes:
Type % Description
Individual Coding
assignments
60% There are 10 individual coding assignments in this course that will
test core concepts. They are all equally weighted and will all be
administered via Gradescope.
All coding assignments will be autograded.
Exams 30% There are 2 midterm exams, which are equally weighted. They
will both be timed exams.
Quizzes 10% This course has 13 quizzes, which are all autograded in Canvas.
These will be equally weighted.
Please read the instructions for each assignment very carefully to make sure you know where to
submit to receive credit!
This course is not curved. Your overall score is computed as 0.3 * total exam score + 0.6 * total
homework score + 0.1 * total quiz score. Here is how we assign letter grades based on your overall
score:
Score Grade
>= 98 A+
93-98 A
90-93 A-
CIT 5210 - Artificial Intelligence | Spring 2024 v1 | Property of Penn Engineering
87-90 B+
83-87 B
80-83 B-
75-80 C+
70-75 C
65-70 C-
50-65 D
below 50 F
Late Policy
If students submit their assignments later than the stated deadline, they will face a 10% penalty per
late day, till their score hits zero. For extraordinarily extenuating circumstances, please fill
out the extension request form located under the Course Resources page in Canvas.
Please fill out the form for any and all extension requests you wish to submit. These
extension requests must be submitted at least 24 hours before the assignment
deadline. If your request is granted, you will see updated deadlines reflected in
Canvas and Gradescope by Mondays at 5 pm ET. You will need to fill out the form for
each assignment you wish to have an extension for.
Regrade Requests
Regrade requests are allowed up to 1 week after the grades are released. These requests must be made
through Gradescope.
We welcome and encourage regrade requests, however, we kindly ask that you review your
work thoroughly before submitting a request. Students have access to three unsuccessful
requests, subsequent unsuccessful requests will result in a 1% deduction from your
assignment grade. Thank you for your understanding and cooperation.
Other Course Activities
The following activities are not mandatory but will greatly support your success on the graded
assignments.
Discussion Forum
Discussion forums are designed to give you optional extra practice with the material, and to see
examples of how your classmates are thinking and working.
CIT 5210 - Artificial Intelligence | Spring 2024 v1 | Property of Penn Engineering
Additional Segments
The professor may add additional optional segments to support the class as needed, including recitation
materials.
Getting and Giving Help
TA and Faculty Support
TAs will hold office hours weekly where they will open a queue in PennLab’s OHQ.io system Canvas
- OHQ.io Resource. Your instructor will hold a weekly Open Office Hour session. He will be
available for a limited number of private meetings per week, depending on the needs of the class.
Collaboration Guidelines
In the professional world of software development, collaboration—including using code that others
have written—is both practical and ubiquitous.
However, to prepare for entering that professional context, you need to develop a full set of software
development skills so that you are both able to create your own code and evaluate the quality of
someone else’s code that you might use.
In the context of this course, independent work and evaluation are critical. Do not collaborate with
others on individual graded assignments unless it is explicitly indicated. The inappropriate
collaboration will be considered cheating and considered under Penn’s Code of Academic Integrity.
Unless otherwise noted, you are not allowed to work in groups on the homework assignments. You
can discuss homework problems with others (you must explicitly list who you discussed problems with
on each homework submission), but all code must be your own independent work. You are not
allowed to upload your code to publicly accessible places (like public github repositories), and you are
not allowed to access anyone else’s code. If you discover someone else’s code online, please report it to
the course staff via a private note on Ed Discussion.
Discussion forums and recitations are collaborative—please take advantage of those times to work with
your colleagues. For general communication with your colleagues, use your Slack channels or Slack
direct messages.
Forming study groups to understand the material is also a good idea, as long as you stay on the
conceptual level and are not collaborating on the graded assignments directly.
CIT 5210 - Artificial Intelligence | Spring 2024 v1 | Property of Penn Engineering
Note: When in doubt always ask the instructor or TA first, to avoid any potential collaboration that
can lead to academic dishonesty.
Do not cheat. Please note that searching for solutions online is the same as cheating. Posting solutions
online is also considered cheating. If you are caught posting solutions or code to a publicly accessible
location (like StackOverflow or GitHub), it will be considered cheating. If you do use GitHub (or
similar cloud-based code management system) to set up a remote code repository, YOU ARE
REQUIRED TO KEEP THAT REPOSITORY PRIVATE.
You can further read Penn's Code of Academic Integrity page on this subject matter, as well as the
SEAS Graduate Student guidelines on the code of ethics.
Plagiarism Policy
The first instance of homework plagiarism will be handled by the instructor and may include escalation
to the Center for Community Standards and Accountability.
Second instances or exam plagiarism will be turned over immediately to the University of Pennsylvania
Center for Community Standards and Accountability.
Regardless of previous work in the course, the penalty for plagiarism is the failure of the course
(regardless of current course average), and potential permanent notation on your academic record that
will follow you to all future academic institutions and possibly future employers. If you are unfamiliar
with what constitutes plagiarism at Penn, visit Penn’s Code of Academic Integrity.
Please note that searching for solutions or code online is a violation of academic
integrity. Sharing solutions or code with another student (unless working on a group
project or other collaborative assignment) is also a violation of academic integrity.
This includes posting solutions and code publicly online, even after you've
completed the course. If you discover publicly viewable solutions for the
assignments of this course, please let the course staff know immediately. Ignorance
of this policy is not an excuse for failing to abide by it.
Guidelines for the Use of Generative AI in this Course
We recognize the increasing prevalence and power of Artificial Intelligence (AI) tools, and
encourage their responsible and ethical use to enhance your learning experience. However, it's
essential to develop a strong understanding of fundamental processes before relying on AI. To
that end:
CIT 5210 - Artificial Intelligence | Spring 2024 v1 | Property of Penn Engineering
Examples of acceptable uses of AI
• Comprehension and Expansion: You are encouraged to use AI to clarify and expand
your understanding of course materials.
• Research and Information Gathering: AI can supplement your research and information
gathering, aiding your exploration of complex concepts.
Examples of unacceptable uses of AI
• Assignment Completion: Avoid using AI for generating code or text for assignments,
peer reviews, or content summaries.
• Assessment Assistance: Do not seek AI help for quizzes, exams, or assessments.
• Academic Integrity: Do not present AI-generated content without citation and as your
own work.
Engaging in unacceptable use of AI tools will result in academic consequences, which may
include grade penalties, academic warnings, or other actions as determined by your instructor
and the university’s academic integrity policies.
Note that these guidelines may differ from those in other courses. If you have questions or
concerns, don't hesitate to reach out. Ultimately, AI can be a valuable educational tool when
used responsibly and aligned with our course policies.
Access to Materials and Content Before and After Graduation
If you would like to retain copies of your submitted assignments for personal use (please
do not make them public), you must download them from Gradescope, Canvas, Codio, and
any other platforms that you submit to during the semester in which you are taking that
course.
Access to course materials and your submissions is not guaranteed after the completion of
a course. Therefore, we recommend that students download any assignments or materials
they would like to keep before a course concludes.
Recording Notice
Public office hours, recitations, and other live events will be recorded, used, and may be made
available to class participants during the current semester as well as students who take the
class in future semesters.
Private office hours will also be offered and are not recorded. Students who do not wish to
attend the publicly-recorded office hour may attend the private office hours.
CIT 5210 - Artificial Intelligence | Spring 2024 v1 | Property of Penn Engineering
Spring 2024 Course Schedule and Important Dates
All dates are subject to change. Please check Ed Discussion for announcements regarding schedule
changes. Also, please check the Canvas Calendar for the most up-to-date information on due dates
etc.
Important to note* Homework assignments are due on Mondays. Quizzes are due on Sundays.
Tuesday, 1/16 -
Monday, 1/22
Module 1 Assignment(s)
Agent Quiz Due, Python Skills Quiz Due
Homework 1 Due
Tuesday, 1/23 -
Monday, 1/29
Module 2 Assignment(s)
Uninformed Search Quiz Due
Homework 2 Due
Tuesday 1/30 -
Monday 2/5
Module 3 Assignment(s)
Informed Search Quiz Due
Homework 3 Due
Tuesday 2/6 -
Monday 2/12
Module 4 Assignment(s)
Game and Adversarial Search Quiz Due
Homework 4 Due
Tuesday 2/13 -
Monday 2/19
Module 5 Assignment(s)
Constraint Satisfaction Problem Quiz Due
Homework 5 Due
Tuesday 2/20 -
Monday 2/26
Module 6 Assignment(s)
Logical Agents Quiz Due
Midterm Exam Due
Tuesday 2/27 -
Monday 3/4
Module 7 Assignment(s)
Markov Decision Processes Quiz Due
Homework 6 Due
Tuesday 3/11 -
Monday 3/18
Module 8 Assignment(s)
Reinforcement Learning Quiz Due
Homework 7 Due
CIT 5210 - Artificial Intelligence | Spring 2024 v1 | Property of Penn Engineering
Tuesday 3/19 -
Monday 3/25
Module 9 Assignment(s)
Probabilities and Language Models Quiz Due
Homework 8 Due
Tuesday 3/26 -
Monday 4/1
Module 10 Assignment(s)
Probabilistic Reasoning and Bayes’ Net Quiz Due
Tuesday 4/2 -
Monday 4/8
Module 11 Assignment(s)
Naïve Bayes and Perceptrons Quiz Due
Homework 9 Due
Tuesday 4/9 -
Monday 4/15
Module 12 Assignment(s)
Neural Net Quiz Due
Tuesday 4/16 -
Monday 4/22
Module 13 Assignment(s)
Natural Language Processing Quiz Due
Homework 10 Due
Tuesday 4/23 –
Sunday 4/28
Module 14 Assignment(s)
End of Term Exam Due
