Applied Machine Learning (CS 441) – Spring 2023

  

 

  Instructor:  Derek Hoiem

 

  Lectures: Tues/Thurs 9:30-10:45, 1002 ECE Building

 

  Syllabus

  Lecture Recordings  

  Lecture Review Questions and Answers

  CampusWire Discussion (code: 6897)

  Canvas Submission

 

  Textbook: Applied Machine Learning by David Forsyth

                                                                                                           

  

   Assignments

HW 1 – Intro to Classification and Regression (Feb 6)

HW 2 – Trees, Ensembles, and MLPs (Feb 27)

HW 3 – Application Domains and Foundation Models (Mar 27)

HW 4 – Pattern Discovery (Apr 17)

Final Project (May 3)

 

 

 

 

 

 

  Class Schedule   (subject to change)

Week

Date

Topic

Link

Reading/Notes

1

Jan 17 (Tues)

Introduction

ppt ; pdf

Jupyter notebook tutorial vid ipynb cc

Numpy tutorial vid cc

Linear algebra tutorial vid  cc

 

 

Supervised Learning Fundamentals

 

 

1

Jan 19 (Thurs)

KNN, key concepts in ML

ppt ; pdf

AML Ch 1

2

Jan 24 (Tues)

Probability and Naïve Bayes

ppt ; pdf

AML Ch 1

2

Jan 26 (Thurs)

Linear Least Squares and Logistic Regression

ppt ; pdf

AML 10.1-10.2, 11

3

Jan 31 (Tues)

Decision Trees

ppt ; pdf

AML Ch 2

3

Feb 2 (Thurs)

Consolidation and Review

 

 

 

Feb 6 (Mon)

HW 1 (Classification & Regression) due

 

 

4

Feb 7 (Tues)

Ensembles and Random Forests

 

AML Ch 2, Ch 12

4

Feb 9 (Thurs)

SVMs and SGD

 

AML Ch 2

5

Feb 14 (Tues)

Neural nets: MLPs, backprop

 

AML Ch 16

 

 

Application Domains

 

AML Ch 16

5

Feb 16 (Thurs)

Deep Networks

 

6

Feb 21 (Tues)

CNNs in Computer Vision

 

AML Ch 17-18

6

Feb 23 (Thurs)

Language Models

 

 

7

Feb 27 (Mon)

HW 2 (Trees & MLPs) due

 

 

7

Feb 28 (Tues)

Transformers in Language and Vision

 

 

7

Mar 2 (Thurs)

Foundation Models: CLIP and GPT-3

 

 

8

Mar 7 (Tues)

Task and Domain Adaptation

 

 

8

Mar 9 (Thurs)

Exam 1 (on PrarieLearn)

 

 

9

Mar 11-19

Spring Break (no classes)

 

 

10

Mar 21 (Tues)

Audio

 

Audio Deep Learning

10

Mar 23 (Thurs)

Fairness and impact on society

 

 

11

Mar 27 (Mon)

HW 3 (Application Domains) due

 

 

11

Mar 28 (Tues)

Big Data and Dataset Bias

 

 

 

 

Pattern Discovery

 

 

11

Mar 30 (Thurs)

K-Means, KD-tree, LSH

 

AML Ch 8

12

Apr 4 (Tues)

Missing Data and EM

 

AML Ch 9

12

Apr 6 (Thurs)

Density estimation: MoG, Kernels, Hists

 

AML Ch 9

13

Apr 11 (Tues)

Data visualization: PCA and t-SNE

 

AML Ch 11

13

Apr 13 (Thurs)

Topic Modeling

 

 

14

Apr 17 (Mon)

HW 4 (Pattern Discovery) due

 

 

14

Apr 18 (Tues)

CCA

 

AML Ch6, Ch 19

14

Apr 20 (Thurs)

TBD

 

15

Apr 25 (Tues)

TBD

 

 

16

Apr 27 (Thurs)

TBD

 

 

16

May 2 (Tues)

Looking Forward & Requested Topics

 

 

16

May 3 (Wed)

Final Project due (cannot be late)

 

 

 

TBA

Final Exam (on PrairieLearn)