Course learning outcomes: Students gain principles of knowledge-based search techniques; automatic deduction, knowledge representation using predicate logic, machine learning, probabilistic reasoning. Students develop applications in tasks such as problem solving, data mining, game playing, natural language understanding, and robotics.

Number of credits associated with the course: 3

How credit hours are met by the course: This class meets for two 75-minute class periods each week over the semester and carries the expectation that students will work on course learning activities (reading, writing, problem sets, studying, etc) for about 3 hours out of classroom for every class period. The syllabus includes more information about meeting times and expectations for student work.

Prerequisite: (COMP SCI 300 or 367) and (MATH 211, 217, 221, or 275) or graduate/professional standing or declared in the Capstone Certificate in Computer Sciences for Professionals.

Time: TR 9:30AM - 10:45AM (Section 001) and TR 2:30PM - 3:45PM (Section 002)

Location: Ingraham Hall 19 (Section 001) and Noland Hall 132 (Section 002)

Textbook: Artificial Intelligence: A Modern Approach (4th edition). Stuart Russell and Peter Norvig. Pearson, 2020. ISBN 978-0134610993. (textbook is optional, but may be useful)

• Understand and be able to apply the foundational tools in Machine Learning and Artificial Intelligence: Linear algebra, Probability, Logic, and elements of Statistics.
• Understand core techniques in Natural Language Processing (NLP), including bag-of-words, tf-idf, n-Gram Models, and Smoothing.
• Understand the basics of Machine Learning. Identify and summarize important features in supervised learning and unsupervised learning.
• Distinguish between regression and classification, and understand basic algorithms: Linear Regression, k-Nearest Neighbors, and Naive Bayes.
• Understand the basics of Neural Networks: Network Architecture, Training, Backpropagation, Stochastic Gradient Descent.
• Learn aspects of Deep Learning, including network architectures, convolution, training techniques.
• Understand the fundamentals of Game Theory.
• Understand how to formulate and solve several types of Search problems.
• Understand basic elements of Reinforcement Learning.
• Consider how Artificial Intelligence and Machine Learning problems are applied in Real - World settings and the Ethics of Artificial Intelligence.

In the regular lecture time, we will have class in either Ingraham Hall 19 (Section 001) or Noland Hall 132 (Section 002), during which the instructor will lecture, the class will enagage in Q&A, quizzes, and discussions.

Each lecture will be a series of short mini-lectures. The lecture will be divided into three blocks. In each block, the instructor will cover some content, and then deliver short quiz questions to clear up any confusion before proceeding to the next block. We would like, whenever possible, all students to participate in the quiz. We also plan to record the lecture and make it available afterwards to watch asynchronously outside of the lecture time. The students can ask questions anytime during the lecture and can post questions on Piazza after class.

We will use Piazza for Q&A outside lectures. Please follow these rules:

• Please check if someone has posted the same / similar question before you; it’s much easier if we build on the thread.
• Use an informative Summary line to help others.

The following weights are used:

• Midterm Exam: 15%
• Final Exam: 15%
• Homework Assignments: 70%

At the end of the semester, the final letter grades are given based on an approximate curve. The weights placed on the assignments will be strictly enforced.

The final letter grade will be assigned based on the percentile of the averaged points in the class:

• A: Top 15-25% of course grades
• AB: next 15-25%
• B: next 0-20%
• BC: next 0-20%
• C: next 0-20%
• D/F: 0-5%

As student performance may vary from semester to semester, the instructors reserve the right to adjust this distribution. McBurney Center students should contact the instructors to specify any special requests for the exams or homework assignments together with the supporting documentation provided by the McBurney Center. We will do our best to accommodate the requests.

Homework assignments include written problems and programming (in Python). Frequently-asked questions (FAQs) on homework assignments will be posted on Piazza. Homework is always due at 9:00am on the due date. Late submissions will not be accepted. Assignment grading questions must be raised with the TAs within 72 hours after it is returned. Note that a regrading request for a part of a homework question may trigger the grader to regrade the entire homework and could potentially take points off. Regrading will be done on the original submitted work, no changes allowed.

TWO lowest homework scores are dropped from the final homework average calculation. This drop is meant for emergency usage. Additional drops, late days, or homework extensions will not be provided. We encourage you to use a study group for doing your homework. Students are expected to help each other out, and if desired, form ad-hoc homework groups. However, each student must produce and turn in their own, unique work.

There will be a midterm exam and a final exam. Makeup exams will not be scheduled. Please plan for exams at these times and let us know about any exam conflicts during the first two weeks of the semester. If an emergency arises that conflicts with the exam times, email us as soon as possible. Emergency exam conflicts will be handled on a case-by-case basis.

Exam grading questions must be raised with the instructor within 72 hours after it is returned. If a regrade request is submitted for a part of a question on the exam, the grader reserves the right to regrade the entire exam and could potentially take points off.

• Midterm : March 9 (5:45pm -- 7:15pm)

• Final : May 12 (5:05pm -- 7:05pm

Instructors / TAs / peer mentors will hold office hours virtually or in-person. See the office hours page for times and locations.