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 14 | Introduction to Data Mining | 1 | Assignment 1 Release |
| Jan 17 | Conceptual Overview | 2+3 | |
| Week 2 | |||
| Jan 21 | Data Manipulation | 2+3 | |
| Jan 24 | Linear Regression | 4 | Assignment 1 Deadline Assignment 2 Release |
| Week 3 | |||
| Jan 28 | Logistic Regression | 4 | |
| Jan 31 | Naïve Bayes | / | Assignment 2 Deadline |
| Week 4 | |||
| Feb 04 | Instance Based Learning | / | |
| Feb 07 | Support Vector Machines | 5 | |
| Week 5 | |||
| Feb 11 | Evaluation Techniques | 3 | |
| Feb 14 | Decision Trees | 6 | Assignment 3 Release |
| Week 6 | |||
| Feb 18 | Ensemble Learning Soft/Hard Voting, Bagging Tree Ensembles: Random Forest |
7 | |
| Feb 21 | Ensemble Learning Boosting, Stacking, ECOC Tree Ensembles: Gradient Boosting |
7 | Assignment 3 Deadline |
| Week 7 | |||
| Feb 25 | Dimensionality Reduction (Online) | 8 | Pool of Papers Release |
| Feb 28 | Neural Networks (Online) | 4+10 | Project Proposal Submission |
| Week 8 | |||
| Mar 04 | Midterm | ||
| Mar 07 | Neural Networks | 4+10 | |
| Week 9 | |||
| Mar 11 | Spring Break | ||
| Mar 14 | Spring Break | ||
| Week 10 | |||
| Mar 18 | Deep Learning | 11-14 | Assignment 4 Release |
| Mar 21 | Deep Learning | 11-14 | |
| Week 11 | |||
| Mar 25 | NLP | 16 | |
| Mar 28 | Time Series | 15 | Assignment 4 Deadline |
| Week 12 | |||
| Apr 01 | Clustering | 9 | |
| Apr 04 | Feature Selection | Paper Critiques Deadline | |
| Week 13 | |||
| Apr 08 | Class Imbalance | ||
| Apr 11 | One Class Learning | 17 | |
| Week 14 | |||
| Apr 15 | Paper Presentations | / | |
| Apr 18 | Paper Presentations | / | |
| Week 15 | |||
| Apr 22 | Final Project Presentation | / | |
| Apr 25 | Final Project Presentation | / | |
| Component | Weight |
|---|---|
| Homework Assignments | 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 | 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.
In regards to Generative AI models such as ChatGPT, you may use them only for homework assignments to assist you in coding the outline of your pipeline or trobleshooting specific parts.
The use of AI models should be reasonable and responsible. For example, AI-generated code may contain technical or conceptual issues that should be manually fixed. Another issue is adopting programming concepts and libraries not seen in class. Both scenarios will be considered as a deviation from the prompt and will be subject to grade penalties.
If you use such models, acknowledge which parts were facilited by AI in your report accompanying the code submission. You should not use them to generate complete solutions to the homework problems in this course that you submit as your own work.
It is reasonable to expect that tools like this will eventually be integrated into the workflows of many businesses in the future, however, while you are still learning the fundamentals of computer science the process of designing machine learning pipelines and writing code is just as important as the final outcome. Complete solutions to coding exercises generated by AI models are considered academic plagiarism, and will be referred to the Academic Integrity Office of American University just as if you had copied the work of a friend, website, or online tutor.