This course presents the main machine learning/data mining algorithms and evaluation methods developed to date in an intuitive way suitable for a non-specialized audience. It also introduces current research developments in the field and initiates students to the solving of applied programs in an innovative way, using existing machine learning and data mining tools.
| Date | Topic | Module / Book Chapter | Deadlines |
|---|---|---|---|
| Week 1 | |||
| Aug 31 | Introduction to Data Mining | 1 | |
| Week 2 | |||
| Sep 7 | Conceptual Overview + Data Manipulation |
2+3 | |
| Week 3 | |||
| Sep 14 | Part I: Naïve Bayes Part II: Instance Based Learning |
/ | |
| Week 4 | |||
| Sep 21 | Part I: Linear and Logistic Regression Part II: Support Vector Machines |
4+5 | |
| Week 5 | |||
| Sep 28 | Part I: Evaluation Techniques Part II: Decision Trees |
3+6 | Assignment 1 Release |
| Week 6 | |||
| Oct 5 | Decision Tree Ensembles | 7 | Assignment 1 Deadline |
| Week 7 | |||
| Oct 12 | Dimensionality Reduction | 8 | Pool of Papers Release |
| Week 8 | |||
| Oct 19 | Neural Networks + Midterm Review | 4+10 | |
| Week 9 | |||
| Oct 26 | Part I: Midterm Part II: Deep Learning |
11+14 | Assignment 2 Release Project Announcement |
| Week 10 | |||
| Nov 2 | Part I: Deep Learning: Review Session Part II: Feature Selection |
11+14 | |
| Week 11 | |||
| Nov 9 | Part I: Clustering Part II: Class Imbalance |
9+17 | Assignment 2 Deadline Project Assignment |
| Week 12 | |||
| Nov 16 | Part I: Time Series Part II: NLP |
15+16 | Paper Critiques Deadline (Nov 20) |
| Week 13 | |||
| Nov 23 | Thanksgiving Holiday | / | |
| Week 14 | |||
| Nov 30 | Paper Presentations (6G + 6UG) | / | |
| Week 15 | |||
| Dec 7 | Final Project Presentation | / | Final Project Deadline |
| Component | Weight |
|---|---|
| Homework Assignments (2) | 25% |
| Midterm Exam | 30% |
| Critiques of 5 research papers | 10% = 5 x 2% |
| Presentation of 1 research paper | 5% |
| Final Project + Presentation | 30% (25% + 5%) |
| Component | Weight |
|---|---|
| Homework Assignments (2) | 20% |
| Midterm Exam | 30% |
| Critiques of 10 research papers | 10% = 10 x 1% |
| Presentation of 2 research papers | 5% = 2 x 2.5% |
| Final Project + Presentation | 35% (30% + 5%) |
Students are recommended to attend all lectures. Prolonged absences must be discussed with the instructor. If you cannot attend lectures regularly, due to work or other obligations during remote learning, then please reach out to the instructor so that I know about it.
Exams cover the material from the lectures, projects, and reading. While not necessarily cumulative, each exam will require understanding many of the concepts covered in the preceding exams. Exams consist of multiple choice, short answer, and long answer questions.
For the Final Project, students will propose their own topic in consultation with the instructor. Project proposals will be due in mid-semester.
| Range | Letter |
|---|---|
| >=93 | A |
| >=90 | A- |
| >=87 | B+ |
| >=83 | B |
| >=80 | B- |
| >=77 | C+ |
| >=73 | C |
| >=70 | C- |
| >=60 | D |
| <60 | F |
Even though we encourage collaboration with a partner, sharing code between groups is strictly forbidden - this is a form of plagiarism. As is showing your work to other students, even just for a second. There is rarely one single correct way to write code that solves a problem. While we want you to feel free to discuss your approach freely with a partner, you should know that there are often many solutions for a given problem and it's typically obvious when one student shares code with another. If you directly copy and paste code from the Internet (or even the text), cite your source in your comments (but also ensure that you understand what the code is doing - not all code on the web is good!). Assignments will be checked using plagiarism detection software and by hand to ensure the originality of the work.
Do not share your code with anyone other than a partner. Do not let someone look at your screen. You may get behind, or your friend may ask for help, but the consequences for plagiarism are far worse than an incomplete submission - for the submission, you will still likely get some points. If I suspect that you have purposely shared code with another student or presented someone else's work as your own, the matter will be referred to the Academic Integrity Code Administrator for adjudication. If you are found responsible for an academic integrity violation, sanctions can include a failing grade for the course, suspension for one or more academic terms, dismissal from the university, or other measures as deemed appropriate by the Dean.
All students are expected to adhere to the American University Honor Code. If you have a question about whether or not something is permissible, ask the instructor or the TA first.
