UC Davis, Computer Science Department, Spring 2018
Dr. Joshua A. McCoy, Assistant Professor
Computer Science and Cinema and Digital Media Departments
Email: jamccoy at ucdavis dot edu
Office: 3033 Kemper Hall
Office Hours: Tuesdays 2:00-3:00pm and Fridays 10:00-11:00am
http://joshmccoy.com
https://faculty.engineering.ucdavis.edu/mccoy/
https://www.twitter.com/deftjams
https://www.twitch.tv/deftjamz
diayu at ucdavis dot edu
Office Hours: Thursdays 7:00 - 8:00 PM at Kemper 53
jain at ucdavis dot edu
Office hours: Tuesdays 1:00 - 2:00 PM at Kemper 3052
Lecture Meeting Time: Mondays, Wednesdays, and Fridays at 9:00-9:50 am in Wellman 106 Grove 1309
A01 Lab: Mondays at 12:10-1:00 pm in Olson 158
A02 Lab: Thursdays at 3:10-4:00 pm in Wellman 212
Final Examination Period: June 12th at 1:00 pm
This course is a elucidation of the theory, techniques and design in the field of artificial intelligence (AI). The course is framed around the major subfields of AI. As the scope of the area is larger than can fit into a single course, the detail covered over topics in this course will weave between conceptual and concrete. AI systems and techniques used in game AI will be be a focus in the course and will be used to frame lectures and motivate examples.
As this is an advanced course in computer science, the work and reading loads will be appropriately advanced and difficult for those students who are newer to the field. Reading in advance of the lectures and leveraging office hours and the provided course communication tools are highly recommended.
- Learn to critically engage challenges with computational thinking via concepts in the area of artificial intelligence.
- Technical and design competency AI techniques.
- Express computational solutions to challenges via the use of and implementation of AI techniques.
- Increase competency with software design while engaging in good programming practices.
- Engage the with history and current state of artificial intelligence.
| Topic | Weeks | Reading and Assignments | Lab Project |
|---|---|---|---|
| Introduction History Overview |
1-2 | Russell and Norvig Chapters 1 and 2 The Knowledge Level by Newell |
Discussion Introduction to Project |
| Search Pathplanning Adversarial |
2-4 | Russell and Norvig Chapters 3, 4, and 5 Jump Point Search with Goal Bounding by Rabin Introduction to Monte Carlo Tree Search |
Introduction to RTS games and StarCraft 2 Project Proposals Team Formation |
| Learning Neural Networks Reinforcement |
4-6 | Russell and Norvig Chapter 18 (overview) Russell and Norvig Chapter 21 (Reinforcement) |
Project Proposals Due Team Formation Complete Group Work Begins |
| Evolutionary Computation Genetic Algorithms |
7 | Evolutionary Computation Ch. 1 & 3 (2 is optional) Optional: The Surprising Creativity of Digital Evolution |
Project Check-ins Workshops |
| Knowledge Representation Concepts Ontologies |
8 | Concepts, Ontologies, and Knowledge Representation Chapters 1,2,3 & 4. | Project Check-ins Workshops |
| Logic Propositional First-order Inference Rules-based Systems |
9 | Introduction to Logic Ch. 2,3,4,5,6 | Project Check-in Workshops |
-
Required: Artificial Intelligence: A Modern Approach 3rd edition by Stuart Russell and Peter Norvig. http://aima.cs.berkeley.edu/
-
Readings From: Evolutionary Computation: A Unified Approach. Available at the UC Davis library here
-
Readings From: Concepts, Ontologies, and Knowledge Representation by Jakus, Grega, Milutinović, Veljko, Omerović, Sanida, and Tomažič, Sašo. Available at the UC Davis library here
-
Readings From: Introduction to Logic, Avaiilable at the UC Davis Library here
-
Optional: Artificial Intelligence: Foundations of Computational Agents by David Poole and Alan Mackworth. Freely * available online: http://artint.info
-
Optional: A Modern Formal Logic Primer by Paul Teller. Freely available online: http://tellerprimer.ucdavis.edu/
-
Optional: Artificial Intelligence for Games, Second Edition by Ian Millington and John Funge: http://ai4g.com/
-
Optional: Logic in Action Open Course Project. Freely available online: http://www.logicinaction.org/
Your performance in this course will be assessed in 5 areas with the following weights on a 100 point scale:
| Area | Points |
|---|---|
| Quizzes and Homework | 40 |
| Project | 30 |
| Examination | 30 |
| Grade | Point Threshold |
|---|---|
| A | 95 |
| A- | 90 |
| B+ | 87 |
| B | 84 |
| B- | 80 |
| C+ | 77 |
| C | 74 |
| C- | 70 |
| D+ | 67 |
| D | 64 |
| D- | 60 |
| F | 0 |
There will be a quiz and a homework for each major topic in the course and will comprise the largest portion of your grade. Each quiz will be weighted equally with respect to your quiz and homework total score. Quizzes will be taken in either lecture or lab meetings and will be announced ahead of time. All students are required to complete the homework assignments. Homework will not be graded but valid attempts must be made on each question. If the homework is missing or incomplete, the score of the related quiz may be penalized. Homework will be submitted via GradeScope.
It is important to be exposed to code of professional quality as often as possible. To achieve this exposure, some homework will require reading and interpreting complex codebases. As you are students studying computer science, there is no shame not being able to completely interpret complex programs. Please seek help from the TAs, the instructor, and your peers if there are parts of these codebases you do not understand. Take solace in the fact that most of the class will struggle with these interpretations.
This course features quarter-long group project featuring the intersection of StarCraft II and the artificial intelligence techniques discussed in class.
| Project Deliverables | Point Allocation |
|---|---|
| Project proposal | 20 |
| Code tour of Solution | 30 |
| Individual weekly development logs | 20 |
| Project report | 15 |
| Website containing all deliverables | 10 |
| Confidential peer assessment | 5 |
sample outline presentation criteria advice for a good project
- meet often
- get started early
- communicate about difficulties
- commit code often / use version control (github recommended)
There will be one comprehensive examination over the topic areas covered by the course. The examination will take place during one or more lecture meetings and may have a take-home component. The homeworks and quizzes are design to serve as the practice exam as well as assessments.
You will have 7 calendar days after grades have been returned to ask for a regrade. Regrades requests will be handled by the TAs and inquiries should be directed to them.
For material graded before this policy was announced, you will have until Monday, May 7th to make your requests.
There will be period extra credit opportunities associated with the contents of the course. Likely extra credit activities are attending talks that prominently feature AI, participating in user studies or play testing sessions, and participating in external activities related to AI such as the potential UC Davis StarCraft 2 AI team. The requirements and amount of extra credit rewarded will depend on the specifics of each opportunity.
This course will make use of several external online resources. The UC Davis Code of Academic Conduct applies to your interactions on these resources.
The official course material and syllabus are on GitHub: https://github.com/dr-jam/ECS170
After coursework is assessed, grades will be placed on Canvas. This service may also be used for selective assessment purposes.
Piazza will be used for discussion outside of lectures, labs, and office hours. After signing up, you can access the Piazza instance for this course here:
https://piazza.com/uc_davis/spring2018/ecs170/home
Use the following information to join:
- Sign-up link: https://piazza.com/uc_davis/spring2018/ecs170
- Sign-up code: ecs170
GradeScope is where you will submit your homework in PDF format. Use the following:
- Sign-up link: https://gradescope.com/courses/17415
- Entry code: 9D5NX6
The UC Davis Code of Academic Conduct (http://sja.ucdavis.edu/files/cac.pdf) will be strictly enforced in this class. In particular, plagiarism, academic dishonesty, and cheating will be dealt with severely. Any breach of the Code of Academic conduct can result in failing the assignment, failing the course, and displinary action via the Office of Student Support and Judicial Affairs (http://sja.ucdavis.edu/).
The use of laptops and technology in general are encouraged in this course as long as they are not disruptive to the rest of the class. If you choose to use a device with a screen, please sit in the back of the room to avoid distracting your fellow students. You are required to ask for permission before video or audio recording in the classroom. In general, students will be treated as adults capable of managing their technological lives while being respectful of others in the classroom.
Students are not permitted to make visual or audio recordings, including live streaming, of classroom lectures or any class related content, using any type of recording devices (e.g., smart phone, computer, digital recorder, etc.) unless prior permission from the instructor is obtained, and there are no objections from any of the students in the class. If permission is granted, personal use and sharing of recordings and any electronic copies of course materials (e.g., PowerPoints, formulas, lecture notes and any classroom discussions online or otherwise) is limited to the personal use of students registered in the course and for educational purposes only, even after the end of the course.
To supplement the classroom experience, lectures may be audio or video recorded by faculty and made available to students registered for this class. Faculty may record classroom lectures or discussions for pedagogical use, future student reference, or to meet the accommodation needs of students with a documented disability. These recordings are limited to personal use and may not be distributed (fileshare), sold, or posted on social media outlets without the written permission of faculty.
Unauthorized downloading, file sharing, distribution of any part of a recorded lecture or course materials. or using information for purpose other than the student's own learning is prohibited unless prior authorization is given by the instructor.