Statistics 652: Statistical Learning (2 units)

Department of Statistics and Biostatistics, CSU East Bay

Author

Prof. Eric A. Suess

Published

January 21, 2026

Syllabus

Lecture:

  • Section 1: MW noon to 1:40, North Science, Rm. N112
  • Section 2: MW 8:00 to 9:40, online on Zoom

Instructor:

Prof. Eric A. Suess

Office: NSc 319 Phone: 510-885-3879 e-mail: eric.suess@csueastbay.edu

Office Hours:

TTh 10 - 11am online on Zoom, or by appointment

Communicating:

Email is the preferred method of communication. Please do not send me emails from within Canvas. Class website will be updated weekly with class topics, homework assignment, and other useful information. Assignment grades will be provided in Canvas. Grades will be posted in Canvas.

Course Description:

Statistical machine learning overview. Choosing a learning algorithm. Unsupervised This is a typo, it is should be Supervised, excluding k-means which is Unsupervised. learning including classification methods, nearest neighbors, naïve Bayes, decision trees and rules, neural networks, k-means. Supervised learning including linear regression and logistic regression. Model performance and evaluation. Confusion matrix. Report writing.

Prerequisites:

  • Post-baccalaureate standing.

Class Website:

Final Exam Dates:

Required Text:

Reference Texts:

  • Géron, Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow, 3rd Edition, O’Reilly, 2022.
  • Géron, Hands-On Machine Learning with Scikit-Learn and PyTorch, O’Reilly, 2025.
  • James, Witten, Hastie, Tibshirani, An Introduction to Statistical Learning, with Applications in R, 2ed, Springer, 2023.
  • James, Witten, Hastie, Tibshirani, An Introduction to Statistical Learning, with Applications in Python, Springer, 2023.

Further References:

Technical Requirements:

Access to a modern computer and permission to install software, Python, Anaconda, RStudio. Access to Colab from your personal Google account. Access to the internet.

Material To Be Covered:

This is the third course in the sequence of Data Science courses offered by the Department of Statistics and Biostatistics for the M.S. Data Science Concentration. The Data Science courses are specifically for registered students in the M.S. Statistics program.

The sequence of courses are:

  1. Stat. 650 Advanced R for Data Science
  2. Stat. 651 Data Visualization
  3. Stat. 652 Statistical Learning
  4. Stat. 653 Statistical Natural Language Processing
  5. Stat. 654 Introduction to Applied Deep Learning

These courses are intended to be taken in order as they build upon each other, but you can discuss taking the courses out of order with instructor approval.

The topics of the course will follow the topics presented in the Modern Data Science with R book. The book will be used as the primary text for Statistics 650, 651, 652, 653. For each class there will be other supporting reference materials.

The main topics for Statistics 652: Statistical Learning

  • Chapter 9. Statistical Foundations
  • Chapter 10. Predictive Analytics
  • Chapter 11. Supervised Learning
  • Chapter 12. Unsupervised Learning
  • Chapter 13. Simulation
  • Chapter 21. Epilogue: Towards ‘’big data’’
  • Appendix E. Regression Modeling

Homework:

The material in this course can only be learned through working many problems. Most of the homework assigned during the course will be graded for content and clarity. Comments will be made and graded problems submitted that need further work should be re-submitted for a final grading.

Homework will be assigned weekly on Mondays. Homework will be due on the following Monday, which means you should complete the homework and come to class prepared to ask questions. Homework will be submitted though Canvas.

Quizzes and Exams:

Two short quiz, one midterm will be given and the final.

Grading:

= 90% A, >= 80% B, >= 70 C, >= 60% D, <60% F

  • Project 30%
  • Homework 15%
  • Quizzes 5%
  • Midterm 25%
  • Final 25%

Policy on Make-up Exams:

You are expected to take the quizzes and exams at the scheduled times. In case of genuine emergency, illness or hardship, for which you can present written documentation I may agree to arrange for a make-up exam. Make-up exams must always be arranged BEFORE the regular exam is given and always take place AFTER the regular exam. Quizzes may not be made up!

Academic Honesty:

  • You are encouraged to work together on homework problems. However, each student must write up the solutions independently. Copying of solutions is not acceptable.
  • You are encouraged to study together for the exams. However, each student must take the exam independently.
  • Cheating will not be tolerated. Any student caught cheating will receive a reduced grade or zero for the assignment or exam in question. In addition, the student will be reported to the University for further disciplinary action.

AI Policy: Use of Generative AI is generally allowed in the course

Use of generative AI is generally welcomed in this course with appropriate citations. AI tools can be used to help brainstorm, edit, or even revise assignments. Assignments submitted without any citation of an AI tool will be considered your own original work. Assignments submitted that utilized AI tools should be appropriately referenced, otherwise it may be considered a violation of the CSUEB Academic Dishonesty Policy and may be subject to disciplinary action.

Student Learning Outcomes (SLO’s):

Student Learning Outcomes - Upon successful completion of this course students will be able to:

  1. Use software to learn from data.
  2. Critically evaluate learning models.
  3. Extract data from large data source to learn from data.
  4. Create reproducible reports.

Student Services:

To access student services offered at Cal State East Bay, click on the MyCompass to get you to your one-stop online student support hub for information on academic advising, tutoring, financial aid, the library, the health center, technology support, career counseling, campus life, equity programs, and more.

Grade Appeal and Academic Grievances:

If you wish to appeal your course grade at the end of the semester or have other academic concerns related to a course, please visit the Grade Appeals and Academic Grievances (GAAG) section of the catalog, which explains the process. URL: https://catalog.csueastbay.edu/index.php?catoid=31