Method
Live-Online
Term
WINTER
Units
3.0 QUARTER UNITS
Cost
$980

Skills you will gain

  • ML Problem Formulation: Identify and define machine learning problems to be solved.
  • Algorithm Implementation: Understand and implement core ML algorithms like regression, classification, and clustering.
  • Algorithm Insights: Explain the workings and practical benefits of various ML techniques.
  • Performance Analysis: Analyze and evaluate the performance of ML solutions on real-world datasets.
  • Hands-on Python Programming: Gain experience with Python-based libraries like Pandas, NumPy, and Sklearn to solve ML problems.

Course Description

Machine learning powers the recommendations, fraud detection, and pricing algorithms you encounter every day. This course builds your intuition for classical and modern ML algorithms, regression, classification, and clustering, using Python libraries like Pandas, NumPy, and Sklearn. Work through hands-on, data-driven applications and complete a final project, gaining a practical foundation for a career in data science and AI.

Prerequisites / Skills Needed

Prerequisites:

  • DBDA.X427: Python for Machine Learning
Syllabus Library
You may enroll in this offering at any time.
Xi "Bill" Chen
Xi "Bill" Chen
Jan. 12 - Mar. 16, Tue, 6:00pm - 9:00pm
  • Live-Online Attend via Zoom at scheduled times.
AISV.X400.(48)
Schedule
Date Start Time End Time Meeting Type Location
Tue, 01-12-2027 6:00pm 9:00pm Live-Online REMOTE
Tue, 01-19-2027 6:00pm 9:00pm Live-Online REMOTE
Tue, 01-26-2027 6:00pm 9:00pm Live-Online REMOTE
Tue, 02-02-2027 6:00pm 9:00pm Live-Online REMOTE
Tue, 02-09-2027 6:00pm 9:00pm Live-Online REMOTE
Tue, 02-16-2027 6:00pm 9:00pm Live-Online REMOTE
Tue, 02-23-2027 6:00pm 9:00pm Live-Online REMOTE
Tue, 03-02-2027 6:00pm 9:00pm Live-Online REMOTE
Tue, 03-09-2027 6:00pm 9:00pm Live-Online REMOTE
Tue, 03-16-2027 6:00pm 9:00pm Live-Online REMOTE
 

This class is offered in an online synchronous format. Students are expected to log into this course via Canvas at the start time of scheduled meetings and participate via Zoom, for the duration of each scheduled class meeting.

To see all meeting dates, click “Full Schedule” below.

You will be granted access in Canvas to your course site and course materials approximately 24 hours prior to the published start date of the course.

Required Tools & Materials: None

Recommended Programming Environment: Python, either local computer or Google Colab. Prefer GPU setup.

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Prerequisites / Skills Needed

Prerequisites:

  • DBDA.X427: Python for Machine Learning

This course applies to these programs:

Demo