CS 4320: Introduction to Machine Learning
Fall 2022, 1:30 pm to 2:45 pm on TR in Old Main 326
Course Descriptions
Application of machine learning tools, with an emphasis on solving practical problems, including data cleaning, feature extraction, and supervised and unsupervised machine learning.
Prerequisites
- A solid Python programming skill
- All class assignments will be in Python.
- The Anaconda distribution of Python is recommended
- We will use Jupyter Notebooks for the assignments, course projects, as well as in-class demos
- If you are not familiar with Jupyter Notebooks, please check here.
Course Material
There is no required textbook for the class. Several introductory books cover the topics we will discuss in this course, such as Introduction to Machine Learning with Python, Python Machine Learning, Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow. All books are available online via the library.
More advanced books include The Elements of Statistical Learning (ESL) by Hastie et al., Murphy’s Machine Learning: A Probabilistic Perspective (ML:APP), and Bishop’s Pattern Recognition and Machine Learning (PRML). For books with a bigger focus on data mining, see Introduction to Data Mining (IDM) and Mining of Massive DataSets, or artificial intelligence, check the Artificial Intelligence: A Modern Approach by Rusell and Norvig.
Grading
- Homework (60%)
- Five programming assignments
- Course Project (15%)
- Students do course projects as groups
- Online poster sessions will be hosted
- Final Exam (25%)
Attendance
- Attendance is not mandatory but encouraged
Schedule
| Date | Topic |
|---|---|
| Aug 30 | Introduction |
| Sep 1 | Introduction |
| Sep 6 | Supervised Learning |
| Sep 8 | Data Exploration |
| Sep 13 | Decision Tree |
| Sep 15 | Model Evaluation |
| Sep 20 | Model Evaluation - II |
| Sep 22 | Decision Tree (Optional) |
| Sep 27 | k-Nearest Neighbors |
| Sep 29 | Data Preprocessing - I |
| Oct 4 | Data Preprocessing - II |
| Oct 6 | Linear Models |
| Oct 11 | Linear Models |
| Oct 13 | Hyperparameter Optimization |
| Oct 18 | Classification Metrics |
| Oct 20 | Regression Metrics |
| Oct 25 | Ensembles |
| Oct 27 | End-to-end Machine Learning Project |
| Nov 1 | Feature Importances and Model Interpretation |
| Nov 3 | Feature Engineering |
| Nov 8 | Clustering - I |
| Nov 10 | Clustering - II |
| Nov 15 | Recommender Systems |
| Nov 17 | Ethics |
| Nov 22 | Canceled |
| Nov 24 | Thanksgiving |
| Nov 29 | Project Presentation |
| Dec 1 | Project Presentation |
| Dec 6 | Final Review |
| Dec 8 | Q&A |