1. Course information
  2. Schedule
  • Brightspace LMS
  • MSDS Program Page
  • CUNYfirst
  • Announcements
  • Course information
    • Overview
    • Syllabus
    • Instructors
    • Schedule
    • Textbooks
    • Software
  • Assignments
    • Labs
    • Project
  • Topics
    • 1 - What is Machine Learning?
    • 2 - Bias-Variance Trade-Off
    • 3 - The Linear Model
    • 4 - Classification
    • 5 - Generative Classification Models and Class Imbalance
    • 6 - Resampling and Cross Validation
    • 7 - Regularization and Model Selection
    • 8 - Tree Models
    • 9 - Ensembles of Models
    • 10 - Causal Inference
    • 11 - Model Interpretation and Ethics
    • 12 - Neural Networks
    • 13 - Deep Learning
    • 14 - Unsupervised Learning
    • 15 - Pretrained Models
  • Acknowledgements
  • title: “DATA 622 - Machine Learning and Big Data” editor_options: chunk_output_type: console

Meetup Link:

Click Here to Join the Meetups on Zoom

Course Schedule
Date Start Time Module Slides Video Main Deliverables
Jan 26 06:45PM Introduction to Machine Learning Meetup 1 Slides Meetup 1 Video
Feb 2 06:45PM Bias-Variance Trade-Off Meetup 2 Slides Meetup 2 Video Lab 1
Feb 9 06:45PM The Linear Model Meetup 3 Slides Meetup 3 Video Lab 2
Feb 16 06:45PM Classification Meetup 4 Slides Meetup 4 Video
Feb 23 06:45PM Generative Classification Models and Class Imbalance Meetup 5 Slides Meetup 5 Video Lab 3
Mar 2 06:45PM Resampling and Cross-Validation Meetup 6 Slides Meetup 6 Video Project Proposal
Mar 9 06:45PM Regularization and Model Selection Meetup 7 Slides Meetup 7 Video Lab 4
Mar 16 06:45PM Tree Models Meetup 8 Slides Meetup 8 Video
Mar 23 06:45M Ensemble Models Meetup 9 Slides Meetup 9 Videp Lab 5
Mar 30 06:45PM Causal Inference Meetup 10 Meetup 10 Video
Apr 6 No Meetup (Spring Break) Minimal Viable Product Demo
Apr 13 06:45PM Model Interpretation, Communication, and Ethics Meetup 11 Meetup 11 Video Lab 6
Apr 20 06:45PM Neural Networks Meetup 12 Meetup 12 Video
Apr 27 06:45PM Deep Learning Lab 7
May 4 06:45PM Unsupervised Learning
May 11 06:45PM Pretrained Models Lab 8, Final project Writeup and Demo

Links to Vignettes and Additional Videos

Week Video Link Announcement Link
Week 1 Course Intro Details
Week 1 Python Packages and Dependencies Details
Week 2 Train-Test Splits in sklearn Details
Week 2 Data Preparation: Exploration, Cleaning, and Transformation Details
Week 2 Fitting Models and Pipelines Details
Week 3 statsmodels Vigente Details
Week 4 Logistic Regression with Titanic Dataset Details
Week 5 Naive Bayes Slides and Vignette Details
Week 5 Bank Marketing with Naive Bayes Details
Week 6 NFL Poisson Regression Data Wrangling Details
Week 6 NFL Cross Validation Details
Week 6 NFL Bootstrap Details
Week 7 Ridge Regression with Hitters Details
Week 7 Lasso Regression with Hitters Details
Week 8 Random Forest Vignette Details
Week 9 XGB Boost Vignette 1: Simple CV with Early Stopping Details
Week 9 XGB Boost Vignette 2: Grid Search CV with Early Stopping Details
Week 9 XGB Boost Vignette 3: Random Search CV with Early Stopping Details
Week 11 Causal ML Vignette 1: Simulating Data Details
Week 11 Causal ML Vignette 2: Fitting Sim Data Details
Week 11 Causal ML Vignette 3: Quality Checks and Fit to Data Details
Instructors
Textbooks

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  • Some content on this page was derived from and inspired by the open source Data Science in a Box course materials developed by Mine Çetinkaya-Rundel