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Schedule

Lectures take place on Mondays and Wednesdays where students will engage in lectures given by Professor Taylor and guest lectures. Lectures are on Monday from 1:25PM – 2:40PM ET, Bloomberg 61X and will cover these topics: 

  • Dataset Curation
  • Building an End-to-End ML Pipeline
  • Regression for Predicting Housing Prices 
  • Clustering for Document Retrieval 
  • Classification for Product Recommendation 
  • Deep Learning for Image Search 

Guest lectures will be given by experts working in AI and ML.

Students have 6 late days to use for the semester for assignment submissions (maximum of 2 per assignment), including homework and the final project. After that, the grade will be dropped one letter grade per day late. No exceptions.

Students have 1 week after assignments are returned to make a regrade request (no exceptions). Send an email to Prof. Taylor and Olga. 

Course Schedule

Week # Lecture (M) Lecture (W)
1: Week of 1/22 Lecture 1: Introduction to PAML  Lecture 2: Revisit Preliminaries
2: Week of 1/29 Lecture 3: Building an End-to-End ML Pipeline I  Lecture 4: Dataset Curation 
3: Week of 2/5 Lecture 5: Data Preprocessing I Lecture 6: Data Preprocessing II
4: Week of 2/12 Lecture 7: Introduction to Regression I Lecture 8: Introduction to Regression II
5: Week of 2/19 Lecture 9: Regression for Predicting Housing Prices  Lecture 10: Regression for Predicting Housing Prices
6: Week of 2/26 No Class – February Break Homework Review
7: Week of 3/4 Lecture 11: Introduction to Classification  Lecture 12: Classification for Product Recommendation
8: Week of 3/11 Lecture 13: Classification for Product Recommendation [REMOTE LECTURE]  Lecture 14: Classification for Product Recommendation [REMOTE LECTURE] 
9: Week of 3/18 Lecture 15: Introduction to Clustering  Lecture 16: Clustering for Document Retrieval
10: Week of 3/25 Lecture 17: Clustering for Document Retrieval  Lecture 18: Clustering for Document Retrieval
11: Week of 4/1 No Class – Spring Break No Class – Spring Break
12: Week of 4/8 Lecture 19: Deep Learning Fundamentals I Lecture 20: Deep Learning for Image Search 
13: Week of 4/15 Lecture 21: Final Project Discussion  Guest Lecture – FP Working Day
14: Week of 4/22 Guest Lecture – Tariq Iqbal (UVA) Guest Lecture – Kilian Weinberger (Cornell)
15: Week of 4/29 Guest Lecture – Karen Levy (Cornell) FP Midpoint Report
16: Week of 5/6 Last Day of Instruction No Class
17: Week of 5/13 Final Project Presentation Final Project Report & Code Due