# Course: CS 189 | EECS at UC Berkeley

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[Home](https://eecs.berkeley.edu/) /[Academics](https://eecs.berkeley.edu/academics) /[Courses](https://eecs.berkeley.edu/academics/courses) /CS 189
# CS 189. Introduction to Machine Learning
**Catalog Description:** Theoretical foundations, algorithms, methodologies, and applications for machine learning. Topics may include supervised methods for regression and classication (linear models, trees, neural networks, ensemble methods, instance-based methods); generative and discriminative probabilistic models; Bayesian parametric learning; density estimation and clustering; Bayesian networks; time series models; dimensionality reduction; programming projects covering a variety of real-world applications. 
**Units:** 4
**Related Areas:**
  * [Artificial Intelligence (AI)](https://www2.eecs.berkeley.edu/Research/Areas/AI)


**Prerequisites:** MATH 53 and MATH 54; and COMPSCI 70 or consent of instructor.
**Formats:**  
Summer: 6.0 hours of lecture and 2.0 hours of discussion per week  
Fall: 3.0 hours of lecture and 1.0 hours of discussion per week  
Spring: 3.0 hours of lecture and 1.0 hours of discussion per week
**Grading Basis:** Student Option
**Final Exam Status:** Yes
* * *
**Class Schedule (Fall 2026):**   
CS 189/289A – TuTh 17:00-18:29, Dwinelle 155 – [Joseph E. Gonzalez](https://www2.eecs.berkeley.edu/Faculty/Homepages/jegonzal.html), [Narges Norouzi](https://www2.eecs.berkeley.edu/Faculty/Homepages/norouzi.html)
**Class Notes**  
_- FAQ link: https://eecs189.org/fa25/faq/  
  
- Lectures will be recorded  
  
- Time conflicts aren't allowed  
  
- No alternate final will be given  
  
- Seats reserved for students with enrollment permission are not open. They are reserved for students in internal programs. Please DO NOT ask faculty or staff for one of these seats._
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