COURSE SYLLABUS AND OUTLINE
BUSN 215, MWF 10:10am – 11:00am
Office hours: Mon. 1pm – 2pm
Office: ITEB 233
Chao Shang (volunteer)
Office hour: by appointment
Office: ITEB 213
The objective of the course is to introduce basic and advanced concepts in machine learning to students, and enable them to use machine learning methods in real-life applications, and review state of the art literature in machine learning. This course covers basic topics of machine learning, such as supervised learning (classification, regression ranking, feature selection etc), unsupervised learning (clustering, dimension reduction or component analysis etc) and some advanced topics, such as scalable machine learning, or deep learning. This course will also attempt if time allows to expose students to emerging topics in the field, such as multi-task learning, multi-view data analysis, structural learning, etc. Usually these topics will be identified by reviewing the latest publications in top venues. Because of the diversity in machine learning topics, the materials covered in this course may vary among semesters.
The course consists of lectures, paper reviews, quizzes and projects. Lectures will serve as the vehicle for the instructor to introduce concepts and knowledge to students. Paper reviews are used to inform students of the latest research topics and techniques. Quizzes are used to test if certain basic concepts have been mastered. The course may also contain guest lectures from related fields and respective experts. A course project will be used for students to get profound hands-on experience by programming certain machine learning algorithms identified from the recent literature. Participation in lectures is encouraged during the class.
Students are encouraged to form study groups to facilitate discussion. Each group is expected to consist of two to three students. During paper review process, each group can select a paper from the recent machine learning venues provided by the instructor, such as International Conference on Machine Learning, or Neural Information Processing Systems, study and present the paper in class. As part of the course, the students will work on a term project where each group can choose to implement the algorithms discussed in the paper they choose to present, or select one of the papers provided by the instructor to study and implement the algorithms in that respective paper. Each team is required to present in the classroom and submit a project report, which describes whether the algorithm could be replicated in their implementation as stated in the original paper, if not, any potential reason why. This exercise will help the team gain a much deeper insights into certain algorithms and promotes collaborations among team members.
None of the textbooks will be required. However, having one or two of them may complement and expand the materials discussed in lectures. Lectures will come with slide files and tutorial/review papers for students to study after lectures.
30 Lectures by the instructor or invited lecturer
4 Sessions for quizzes
2-3 Sessions for paper review presentations
2-4 Sessions for final Presentations of term projects
Total, 41 sessions plus final exam week
1. Introduction of different learning problems (supervised learning, unsupervised learning, semi-supervised learning, active learning)
2. Introduction of widely used classification or regression methods, such as logistic regression, support vector machine, linear regression, generalized linear regression, shallow neural networks, boosting)
3. Introduction of widely used clustering methods, such as k-means, hierarchical clustering, spectral clustering)
4. Introduction of widely used dimensionality reduction methods, such as principal component analysis, correlational component analysis, independent component analysis
5. Introduction of other dimension reduction methods, such as sparse modeling with regularization conditions: LASSO, 1-norm support vector machine)
6. Discussion of some recent topics: deep learning, multi-task learning, collaborative filtering, multi-view data analysis, matrix completion etc.
Different semesters may cover different subsets of the above topics, depending on the need reflected by a student survey in the beginning of the semester.
1. Computers are allowed in classroom for taking notes or any activity related to the current class meeting.
2. Participation in paper review sessions itself will earn 50% credits for each review assignment. The quality of your presentation slides will be judged by the instructor and constitutes the other 50% credits.
3. Quizzes will be graded by the instructor or her postdoctoral fellow.
4. Final term projects will be graded by the instructor and her senior graduate students based on the clarity and correctness of the project report and the comparison of final presentation of all teams.
5. We may need to use github to manage the final projects, so students are encouraged to get familiar with github.
1. If you cannot attend a review session that you are supposed to present, you need to find your team member to cover you.
2. If a quiz is missed, it should be fine because we drop your lowest quiz score in the final score calculation. If you miss two quizzes, there will be a take-home quiz to make up the credits. If you miss more quizzes, there won’t be any additional make-up.
A HuskyCT site has been 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.
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.