CSE – 4705 - 001




BCH 443, Mon/Wed 3:00pm – 4:15pm




         This class may have 1-2 lab as needed.


            Jinbo Bi

            Phone: 486-1458


            Office hours: Tue 1:00pm – 3:00pm

            Office: ITEB 233


     Huizhong Gao

     Phone: 486-0510 


     Office hours: Mon 4:15-5:15pm

     Lab: ITEB 213



Course website:                


The purpose of this course is to introduce students the basic research areas in artificial intelligence (AI), to study several techniques in depth in selected topics of AI, and to apply these techniques in real-life AI projects that involve big data competitions. AI is a huge field, including many subareas such as knowledge representation, reasoning, machine learning, data mining, robotics and natural language processing etc. This course aims to cover some basic topics as well as some state of the art. Basic areas such as intelligent agents, searching and first-order logic will be studied. However, a significant effort will also be given to learning, learning from massive examples/big data and statistical learning. Throughout the course, substantial projects will be designed that are based on real-life data challenges, and students will be asked to form teams and each team can choose from the designed projects to work towards their course projects.

The course will consist of lectures, demonstrations, term projects and potential laboratories. Lectures will serve as the vehicle to introduce concepts and knowledge to students. Demonstrations aim to bring some concrete sense and experience with how AI works. As part of the course, students will work on a term project with the goal of applying any learning techniques to a problem selected from a list of projects. Teams of four-six students will be created for each project. Each team is required to present in the classroom and submit a project report, which includes the definition of the problem, techniques used to solve the problem and experimental results obtained. This exercise will help the team gain a hands-on understanding of the material studied in this course and promotes collaborations among team members. Programming-based laboratories may be arranged.  If a reasonable number of students are not familiar with the programming language used to complete homework assignments, laboratories can be arranged to help.


  1. Required textbook

Artificial Intelligence: A Modern Approach (3rd edition) by Stuart Russell and Peter Norvig, ISBN-10: 0136042597

This book is a required textbook for this course. It is a large book, providing a broad introduction to many aspects of AI. We will only study a subset of the materials.  Students can read this book to learn other areas that are not covered in this course.


  1. Optional textbooks

a.       Introduction to Data Mining by Pang-Ning Tan, Michael Steinbach, and Vipin Kumar, ISBN-10: 0321321367

b.      Pattern Recognition and Machine Learning (Information Science and Statistics) by Christopher M. Bishop, ISBN-10: 0387310738

The above two books are supplementary to the required text on the machine learning and data mining subareas.  When we cover materials from these two books, slides or lecture notes will be provided for additional reading.


The course in this semester has four components with each component taking roughly 3 weeks. Each component has a homework assignment which may involve computer programming and a in-class quiz which may take 30 to 45 minutes.  This course has no final exam but a final term project.  The instructor will design term projects. Although students are allowed to design their own projects, their projects need to be approved by the instructor in terms of appropriateness for this course. Otherwise, students will choose a project from the pre-designed projects.

  1. Programming-involved homework assignments (4): 30%

In the first part of the course, we will have 2-4 programming assignments, which can be implemented by a specific programming language.  If a student decides to use another programming language, please discuss it with the TA of the course to see whether his assignments can be graded.  The assignment with the lowest score will be dropped for the final grade calculation.

  1. In-class quizzes (4): 30%

The quizzes will be in-class, open-notes/open-book, and at the end of each study component (or one class meeting later to allow students to prepare for the quizzes). No review sessions will be given but occasionally sample questions may be provided beforehand. The quiz with the lowest score will be dropped for the final grade calculation.

  1. Term Project (1): 40%

A team can consist of 5-7 students and students of the team altogether work on a substantial project that is chosen from a list of projects.  At the end, the grade of the term project will depend on a short presentation on the project, a project report (a Word file), and related software package (together with user specification if needed).


Invited talks, student discussion sessions, and computer labs may be included in the schedule later on, depending on the need.

Week 1 Introduction and intelligent agents

Week 2 Searching, uninformed searching

Week 3 Informed searching (project discussion)

Week 4 Informed search, quiz

Week 5 Supervised learning

Week 6 Linear regression models

Week 7 Neural networks with back propagation

Week 8 Unsupervised learning, quiz

Week 9 Clustering

Week 10 Principal component analysis

Week 11 Logical agents, quiz

Week 12 First-order agent, (project discussion)

Week 13 First-order agent

Week 14 Project preparation, quiz, and presentation



1.      Computers are allowed in classroom for taking notes or any activity related to the current class meeting.

2.      Participation in lectures is highly encouraged as quizzes may be based on class discussion.

3.      TA will be responsible for grading all homework assignments, quizzes.

4.      TA and the instructor will grade the final term projects.

5.      TA maintains the grades and will compute the final grade using the designed weights.


1.      No make-up plan is designed for missing homework assignments.  Please do your best to turn them in.

2.      No make-up will be made if you miss one quiz, and if you miss two quizzes (for an acceptable reason), a make-up quiz can be taken.


A HuskyCT site will be set up for the class. You can access it by logging in with your NetID and password.  You must use HuskyCT for submitting assignments.  The instructor uses the HuskyCT announcement to announce class materials, grades, problem clarifications, changes in class schedule, and other class announcements.




Three-four projects will be designed and listed at the course website with necessary materials by the end of the fifth week.  Student teams can choose from these projects.



You are expected to adhere to the highest standards of academic honesty. Unless otherwise specified, collaboration on assignments is not allowed. Use of published materials is allowed, but the sources should be explicitly stated in your solutions. Violations will be reviewed and sanctioned according to the University Policy on Academic Integrity. Collaborations among team members are only allowed for the final term projects that are selected.

“Academic integrity is the pursuit of scholarly activity free from fraud and deception and is an educational objective of this institution. Academic dishonesty includes, but is not limited to, cheating, plagiarizing, fabricating of information or citations, facilitating acts of academic dishonesty by others, having unauthorized possession of examinations, submitting work for another person or work previously used without informing the instructor, or tampering with the academic work of other students.”


If you have a documented disability for which you are or may be requesting an accommodation, you are encouraged to contact the instructor and the Center for Students with Disabilities or the University Program for College Students with Learning Disabilities as soon as possible to better ensure that such accommodations are implemented in a timely fashion.

Jinbo Bi ©2016 /1-2016/1
Last revised: 01/10/2016