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.


  • A solid Python programming skill
    • All class assignments will be in Python.
    • The Anaconda distribution of Python is recommended
      • Please check this and this pages for guidance on installation.
    • 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.


  • Homework (60%)
    • Five programming assignments
  • Course Project (15%)
    • Students do course projects as groups
    • Online poster sessions will be hosted
  • Final Exam (25%)


  • Attendance is not mandatory but encouraged


Aug 30Introduction
Sep 1Introduction
Sep 6Supervised Learning
Sep 8Data Exploration
Sep 13Decision Tree
Sep 15Model Evaluation
Sep 20Model Evaluation - II
Sep 22Decision Tree (Optional)
Sep 27k-Nearest Neighbors
Sep 29Data Preprocessing - I
Oct 4Data Preprocessing - II
Oct 6Linear Models
Oct 11Linear Models
Oct 13Hyperparameter Optimization
Oct 18Classification Metrics
Oct 20Regression Metrics
Oct 25Ensembles
Oct 27End-to-end Machine Learning Project
Nov 1Feature Importances and Model Interpretation
Nov 3Feature Engineering
Nov 8Clustering - I
Nov 10Clustering - II
Nov 15Recommender Systems
Nov 17Ethics
Nov 22Canceled
Nov 24Thanksgiving
Nov 29Project Presentation
Dec 1Project Presentation
Dec 6Final Review
Dec 8Q&A