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Introduction to Machine Learning

SAT Score Range

4 sessions

This series ended on December 2, 2024. All 1:1 and group chats related to this series are disabled 7 days after the last session.

About

[More classes will be added to the series soon!]

This series will provide a practical introduction to machine learning. Topics include regression (linear & polynomial), regularization, classification, clustering, retrieval, recommender systems, and deep learning, with a focus on an intuitive understanding grounded in real-world applications. Intelligent applications are designed and used to make predictions on large, complex datasets.

Prerequisites include a basic understanding of Python syntax and some statistical knowledge. Even if you don't meet these requirements, I will try my best to make it easier to understand.

I will provide datasets and practice problems via GitHub, and I expect everyone to attend Office Hours (optional) if they have any questions. Machine learning is a hard course, but focusing on understanding the concepts will make it easier.

Tutored by

Piyush A 🇺🇸

Certified in 10 topics

View Profile

Hi, I'm Piyush Acharya, the founder of DNAnalyzer, Vice President of Interlake Programming Club, and an Organizer at HackPNW! I'm a junior at Interlake High School and the founder of DNAnalyzer, a GitHub Trending platform that revolutionizes ML-powered DNA analysis and makes it accessible to all. I'm also an Organizer at HackPNW (Washington's largest student-led hackathon organization), Vice President of Interlake Programming Club, and a researcher at UVic, where I apply machine learning techniques to neural radiance fields (NeRFs) and epigenetic data. I love competing in coding and science tournaments, making fun and useful projects, and exploring new technologies. Some of my key achievements include winning the U.S. Department of Energy's National Science Bowl Finals in Washington D.C., being a two-time winner of the U.S. House of Representatives Congressional App Challenge, placing 2nd in the 2024 Science Olympiad Robot Tour event, and being a USACO Gold qualifier. In my spare time, you can find me exploring the latest in technology, biking through the extensive Redmond trail system, or flying my DJI Mavic 3 Pro.

Schedule

✋ ATTENDANCE POLICY

Attendance is not tracked, but do note that the lectures are the best time to understand concepts and ask me questions so it is expected that you come.

SESSION 1

28

Nov

SESSION 1

Artificial Intelligence

Artificial Intelligence

Thu 9:00 PM - 10:00 PM UTCNov 28, 9:00 PM - 10:00 PM UTC

Introduction / Regression
SESSION 2

29

Nov

SESSION 2

Artificial Intelligence

Artificial Intelligence

Fri 9:00 PM - 10:00 PM UTCNov 29, 9:00 PM - 10:00 PM UTC

Regression
SESSION 3

30

Nov

SESSION 3

Artificial Intelligence

Artificial Intelligence

Sat 9:00 PM - 10:00 PM UTCNov 30, 9:00 PM - 10:00 PM UTC

Assessing Performance; Bias + Variance Tradeoff
SESSION 4

2

Dec

SESSION 4

Artificial Intelligence

Artificial Intelligence

Mon 12:30 AM - 1:30 AM UTCDec 2, 12:30 AM - 1:30 AM UTC

Regularization: Ridge

Public Discussion

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Nov 28 - Dec 2

1 week

60 mins

/ session

SCHEDULE

Thursday, Nov 28

9:00PM

Friday, Nov 29

9:00PM

Saturday, Nov 30

9:00PM

Monday, Dec 2

12:30AM