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 1:00PM – 2:15PM 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
Ethics in ML

Guest lectures will be given by experts working in related fields including data curation, AI, ML, and computer vision.

Late assignments will be dropped one letter grade per day late. Students have 6 late days to use for assignments throughout the semester. Students have 1 week after assignments are graded to make a regrade request (no exceptions). Send an email to Prof. Taylor and Jinzhao Kang.

Course Schedule (Tentative)

Week #	Lecture (M)	Lecture (W)
1: Week of 1/23	Introduction to PAML	Revisit Preliminaries
2: Week of 1/30	Building an End-to-End ML Pipeline I	Building an End-to-End ML Pipeline II
3: Week of 2/6	Dataset Creation, Acquisition, and Handling	Dataset Creation, Acquisition, and Handling
4: Week of 2/13	Introduction to Regression I	Introduction to Regression II
5: Week of 2/20	Regression for Predicting Housing Prices I	Regression for Predicting Housing Prices II
6: Week of 2/27	No Class – February Break	Scaling to Large Datasets
7: Week of 3/6	Introduction to Classification I	Introduction to Classification II
8: Week of 3/13	Classification for Product Recommendation I	Classification for Product Recommendation II
9: Week of 3/20	Introduction to Clustering I	Introduction to Clustering II
10: Week of 3/27	Clustering for Document Retrieval I	Clustering for Document Retrieval II
11: Week of 4/3	No Class – Spring Break	No Class – Spring Break
12: Week of 4/10	Deep Learning Fundamentals I	Deep Learning for Image Search II
13: Week of 4/17	Deep Learning for Image Search II	Final Project Discussion
14: Week of 4/24	FP Working Day	Guest Lecture – Tariq Iqbal (UVA)
15: Week of 5/1	FP Working Day	FP Working Day/Final Project Midpoint Report
16: Week of 5/8	Last Day of Instruction	No Class
17: Week of 5/15	Final Project Presentation	Final Project Report & Code Due

Week #

Lecture (M)

Lecture (W)

1: Week of 1/23

Introduction to PAML

Revisit Preliminaries

2: Week of 1/30

Building an End-to-End ML Pipeline I

Building an End-to-End ML Pipeline II

3: Week of 2/6

Dataset Creation, Acquisition, and Handling

Dataset Creation, Acquisition, and Handling

4: Week of 2/13

Introduction to Regression I

Introduction to Regression II

5: Week of 2/20

Regression for Predicting Housing Prices I

Regression for Predicting Housing Prices II

6: Week of 2/27

No Class – February Break

Scaling to Large Datasets

7: Week of 3/6

Introduction to Classification I

Introduction to Classification II

8: Week of 3/13

Classification for Product Recommendation I

Classification for Product Recommendation II

9: Week of 3/20

Introduction to Clustering I

Introduction to Clustering II

10: Week of 3/27

Clustering for Document Retrieval I

Clustering for Document Retrieval II

11: Week of 4/3

No Class – Spring Break

No Class – Spring Break

12: Week of 4/10

Deep Learning Fundamentals I

Deep Learning for Image Search II

13: Week of 4/17

Deep Learning for Image Search II

Final Project Discussion

14: Week of 4/24

FP Working Day

Guest Lecture – Tariq Iqbal (UVA)

15: Week of 5/1

FP Working Day

FP Working Day/Final Project Midpoint Report

16: Week of 5/8

Last Day of Instruction

No Class

17: Week of 5/15

Final Project Presentation

Final Project Report & Code Due