Times and Locations
- Lecture: Mondays and Wednesdays, Wheeler 150 (Auditorium), 5:00-6:30pm
- Discussion Sections: Will be posted on Piazza!
- Exam Prep Sessions: Will be posted on Piazza!
CommunicationThere will be several routes of communication for this course:
The main mode of electronic communication between students and staff, as well as amongst students, will be through Piazza. It is intended for general questions about the course, clarifications about assignments, student questions to each other, discussions about the material, and so on. We strongly encourage students to participate in the discussion, ask, and answer questions through this site. The course staff will monitor discussions closely.
If you need to contact the course staff privately, you should email cs188sp18staff AT lists.berkeley.edu. You may, of course, contact the professor or GSIs directly, but the staff list will produce the fastest response.
- For exceptions (projects, homework, exams) please email Anwar Baroudi, the head GSI,
mabaroudiAT berkeley. edu
This course will introduce the basic ideas and techniques underlying the design of intelligent computer systems. A specific emphasis will be on the statistical and decision-theoretic modeling paradigm.
By the end of this course, you will have built autonomous agents that efficiently make decisions in fully informed, partially observable and adversarial settings. Your agents will draw inferences in uncertain environments and optimize actions for arbitrary reward structures. Your machine learning algorithms will classify handwritten digits and photographs. The techniques you learn in this course apply to a wide variety of artificial intelligence problems and will serve as the foundation for further study in any application area you choose to pursue.
See the syllabus for slides, deadlines, and the lecture schedule.
CS 61A and 61B: Prior computer programming experience is expected (see below); most students will have taken both these courses.
CS 70 or Math 55: Facility with basic concepts of propositional logic and probability are expected (see below); CS 70 is the better choice for this course.
This course has substantial elements of both programming and mathematics because these elements are central to modern AI. You should be prepared to review basic probability on your own if it is not fresh in your head. You should also be very comfortable programming on the level of CS 61B even though it is not strictly required.
CS61A AND CS61B AND CS70 is the recommended background.
Course programming assignments will be in Python. We do not assume that students have previous experience with the language, but we do expect you to learn the basics very rapidly. Project 0 is designed to teach you the basics of Python, but if you want to get a head start here is a good tutorial: ACM Python Tutorial
This class includes six programming projects and regular electronic homework assignments.
Homeworks: Homework will be completed on EdX. Questions have limited submissions, but those submissions can be reset by going to office hours and demonstrating past effort to the TA.
Collaboration: Project 0 is to be completed alone. Projects 1 through 6 can be completed alone or in teams of two. When
Homework is to be submitted individually but may be discussed in groups.
Slip days: Programming projects must be turned in electronically by midnight on the listed due date. You will have a total of 5 slip days for these projects, up to two of which can be used for each project. Note that slip days are counted at the granularity of days, rounded up to the nearest day. For example, for a project due at midnight on Friday, any submission from Friday midnight - Saturday midnight will use up one slip day, any submission from Saturday midnight - Sunday midnight will use up two slip days, and any submissions after Sunday midnight will receive no credit. The other homework assignments do not have late days!
Project grading: Projects will by default be graded automatically for correctness, though we will review projects individually as necessary to ensure that they receive the credit they deserve. To calculate your total project grade, we will consider all possible allocations of slip days across all projects to give you the best score achieved. For example, suppose you submitted a 24/25 on Project 1 on
Ethics: Submissions should acknowledge all collaborators and sources consulted. All code and written responses should be original. We trust you all to submit your own work, but to protect the integrity of the course from anyone who doesn’t want to play by the rules, we will actively be checking for code plagiarism (both from current classmates and previous semesters). I’m not lenient about cheating; I sympathize with Kris Pister’s policy.
There will be one midterm and a final.
The midterm will be held on Wednesday, March 14th from 7-9 PM (currently tentative)
Locations are currently TBD, information will be updated here as it is finalized
The final will take place on Friday, May 11th from 3-6 PM.
There will be no make-up exams.
Overall grades will be determined from:
- Programming Assignments (30%)
- Electronic Assignments (10%)
- Midterm Exam (25%)
- Final Exam (35%)
Grades are on the following fixed scale:
|A||[90 – 100]%|
|A-||[85 – 90)%|
|B+||[80 – 85)%|
|B||[75 – 80)%|
|B-||[70 – 75)%|
|C+||[65 – 70)%|
|C||[60 – 65)%|
|C-||[55 – 60)%|
|D+||[50 – 55)%|
|D||[45 – 50)%|
|D-||[40 – 45)%|
|F||[0 – 40)%|
These cutoffs represent grade minimums. We may adjust grades upward based on class participation, extra credit, etc. The grade of A+ will be awarded at the professor’s discretion based on exceptional performance.
Regrade Policy: If you believe an error has been made in the grading of one of your exams or assignments, you may resubmit it for a regrade. Regrades for cases where we misapplied a rubric in an individual case is much more likely to be successful than regrades that argue about relative point values within the rubric, as the rubric is applied to the entire class. Because we will examine your entire submission in detail, your grade can go up or down as a result of a regrade request.
No textbook is required for this course. For students who want to read more, we recommend Artificial Intelligence: A Modern Approach by Stuart Russell (UC Berkeley) and Peter Norvig (Google). Be aware, however, that this is not a course textbook, so our presentation does not necessarily follow the presentation in the book.