Archives


This page contains the previous semesters’ course schedule, contents, and staff information.

Fall 2018

Introduction to Machine Learning for Python. It was the semester when we published a brand-new course website. It was also the first semester we established an interactive classroom setting by creating in-class assignments, and held a hackathon as a final project (sponsored by Google, provided Google Home Mini and gift cards to the winners).

Education Lead: Jared Junyoung Lim (CS '20)
Lecturers: Abby Beeler (CS '20), Ethan Cohen (INFO '19), Jared Junyoung Lim (CS '20)
TAs: Ann Zhang (CS & Econ '20), Chris Elliott (INFO '20), Dylan Tsai (CS '21), Shubhom Bhattacharya (ECE '20), Tanmay Bansal (CS '21)


Date Lecture Title Lecture Contents Lecture Notes Assignment
02/07/18 - - LECTURE 0: SETUP
-
02/14/18 Lecture 1: Introduction and Data Manipulation LECTURE 1: SLIDES
LECTURE 1: NUMPY
LECTURE 1: PANDAS
Take Home Quiz
02/21/18 Lecture 2: Manipulation Techniques and Visualization LECTURE 2: SLIDES
LECTURE 2: DEMO
LECTURE 2: VISUAL
LECTURE 2: DATASET
Project A

Spring 2018

Introduction to Machine Learning for Python. It was the semester when we rebranded the CDS Education team.

Education Lead: Jared Junyoung Lim (CS '20)
Lecturers: Jared Junyoung Lim (CS '20), Abby Beeler (CS '20)
TAs: Ann Zhang (CS & Econ '20), Ethan Cohen (INFO '19), Ryan Kannanaikal (MATH '20), Shubhom Bhattacharya (ECE '20)


Date Lecture Title Lecture Contents Lecture Notes Assignment
02/07/18 - - LECTURE 0: SETUP
-
02/14/18 Lecture 1: Introduction and Data Manipulation LECTURE 1: SLIDES
LECTURE 1: NUMPY
LECTURE 1: PANDAS
Take Home Quiz
02/21/18 Lecture 2: Manipulation Techniques and Visualization LECTURE 2: SLIDES
LECTURE 2: VISUAL
LECTURE 2: DEMO
LECTURE 2: DATASET
Project A
02/28/18 Lecture 3: Introduction to Machine Learning LECTURE 3: SLIDES
LECTURE 3: SUPER
Project A
03/07/18 Lecture 4: Introduction to Classification LECTURE 4: SLIDES
LECTURE 4: CLASS
LECTURE 4: DEMO
LECTURE 4: DATASET
Project B
03/14/18 Lecture 5: SVM LECTURE 5: SLIDES
LECTURE 5: SVM
LECTURE 5: DEMO
Project B
03/21/18 Lecture 6: Logistic Regression and Decision Trees LECTURE 6: SLIDES
LECTURE 6: LOGIT
LECTURE 6: DEMO
Project C
03/28/18 Lecture 7: Unsupervised Learning LECTURE 7: SLIDES
LECTURE 7: UNSUP
LECTURE 7: DEMO
Project C
04/11/18 Lecture 8: Model Optimization LECTURE 8: SLIDES
LECTURE 8: MODEL
Final Project
04/18/18 Lecture 9: Cross Validation & Ensemble Learning LECTURE 9: SLIDES
LECTURE 9: ENSEM
LECTURE 9: BAG
Final Project

Fall 2017

An Introduction to Data Science and Machine Learning. It was the semester we recreated the course in Python. It was also the semester our course got accredited by the Cornell CIS Department and became an official course.

Education Lead: Jared Junyoung Lim (CS '20)
Lecturers: Dae Won Kim (M.Eng ORIE '18), Jared Junyoung Lim (CS '20)
TAs: Abby Beeler (CS '20), Ann Zhang (CS & Econ '20), Cameron Ibrahim (ORIE '20), Kexin (Grace) Zheng (CS '20), Ryan Kannanaikal (MATH '20), Shubhom Bhattacharya (ECE '20)


Date Lecture Title Lecture Contents Lecture Notes Assignment
02/07/18 - - LECTURE 0: SETUP
-
02/14/18 Lecture 1: Introduction and Data Manipulation LECTURE 1: SLIDES
LECTURE 1: NUMPY
LECTURE 1: PANDAS
Take Home Quiz
02/21/18 Lecture 2: Manipulation Techniques and Visualization LECTURE 2: SLIDES
LECTURE 2: DEMO
LECTURE 2: VISUAL
LECTURE 2: DATASET
Project A

Spring 2017

CDS Training Program. Data Analysis and Machine Learning were taught in R. It was the first semester we launched this course.

Education Lead: Dae Won Kim (ORIE '17)
Lecturer: Dae Won Kim (ORIE '17)
TAs: Amit Mizrahi (CS '18), Chase Thomas (INFO '19), Jared Junyoung Lim (CS '20), Kenta Takatsu (CS '19)


Date Lecture Title Lecture Contents Lecture Notes Assignment
02/15/17 Lecture 1: Introduction to R LECTURE 1: SLIDES
LECTURE 1: NUMPY
LECTURE 1: PANDAS
Take Home Quiz
02/22/17 Lecture 2: Manipulation Techniques and Visualization LECTURE 2: SLIDES
LECTURE 2: VISUAL
LECTURE 2: DEMO
LECTURE 2: DATASET
Project A
03/01/17 Lecture 3: Introduction to Machine Learning LECTURE 3: SLIDES
LECTURE 3: SUPER
Project A
03/08/17 Lecture 4: Introduction to Classification LECTURE 4: SLIDES
LECTURE 4: CLASS
LECTURE 4: DEMO
LECTURE 4: DATASET
Project B
03/22/17 Lecture 5: SVM LECTURE 5: SLIDES
LECTURE 5: SVM
LECTURE 5: DEMO
Project B
03/29/17 Lecture 6: Logistic Regression and Decision Trees LECTURE 6: SLIDES
LECTURE 6: LOGIT
LECTURE 6: DEMO
Project C
04/12/17 Lecture 7: Unsupervised Learning LECTURE 7: SLIDES
LECTURE 7: UNSUP
LECTURE 7: DEMO
Project C
04/19/17 Lecture 8: Model Optimization LECTURE 8: SLIDES
LECTURE 8: MODEL
Final Project
04/26/17 Lecture 9: Cross Validation & Ensemble Learning LECTURE 9: SLIDES
LECTURE 9: ENSEM
LECTURE 9: BAG
Final Project
05/03/17 Lecture 9: Cross Validation & Ensemble Learning LECTURE 9: SLIDES
LECTURE 9: ENSEM
LECTURE 9: BAG
Final Project
05/10/17 Lecture 9: Cross Validation & Ensemble Learning LECTURE 9: SLIDES
LECTURE 9: ENSEM
LECTURE 9: BAG
Final Project