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 | |||
| Jan 16 | Introduction to Data Mining | 1 | |
| Jan 19 | Conceptual Overview | 2+3 | |
| Week 2 | |||
| Jan 23 | Data Manipulation | 2+3 | Assignment 0 Release |
| Jan 26 | Linear Regression | 4 | |
| Week 3 | |||
| Jan 30 | Logistic Regression | 4 | Assignment 0 Deadline |
| Feb 02 | Naïve Bayes | / | |
| Week 4 | |||
| Feb 06 | Instance Based Learning | / | |
| Feb 09 | Support Vector Machines | 5 | |
| Week 5 | |||
| Feb 13 | Evaluation Techniques | 3 | |
| Feb 16 | Decision Trees | 6 | Assignment 1 Release |
| Week 6 | |||
| Feb 20 | Ensemble Learning Soft/Hard Voting, Bagging Tree Ensembles: Random Forest |
7 | |
| Feb 23 | Ensemble Learning Boosting, Stacking, ECOC Tree Ensembles: Gradient Boosting |
7 | Assignment 1 Deadline |
| Week 7 | |||
| Feb 27 | Dimensionality Reduction | 8 | Pool of Papers Release |
| Mar 01 | Neural Networks | 4+10 | |
| Week 8 | |||
| Mar 05 | Neural Networks + Midterm Review | 4+10 | |
| Mar 08 | Midterm | ||
| Week 9 | |||
| Mar 12 | Spring Break | ||
| Mar 15 | Spring Break | ||
| Week 10 | |||
| Mar 19 | Deep Learning | 11-14 | Assignment 2 Release |
| Mar 22 | Deep Learning | 11-14 | Project Announcement |
| Week 11 | |||
| Mar 26 | Feature Selection | 14 | Assignment 2 Deadline |
| Mar 29 | Clustering | 9 | |
| Week 12 | |||
| Apr 02 | Class Imbalance | Paper Critiques Deadline | |
| Apr 05 | One Class Learning | 17 | |
| Week 13 | |||
| Apr 09 | Time Series | 15 | |
| Apr 12 | NLP | 16 | |
| Week 14 | |||
| Apr 16 | Paper Presentations | / | |
| Apr 19 | Paper Presentations | / | |
| Week 15 | |||
| Apr 23 | Final Project Presentation | / | |
| Apr 26 | Final Project Presentation | / | |
| Component | Weight |
|---|---|
| Homework Assignments (3) | 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 (3) | 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.
