Applied Machine Learning (CS 441) – Fall 2026

 

Instructor: Derek Hoiem

 

Lectures: Tues/Thurs 12:30-1:45, 0027/1025 CIF

 

Syllabus

Lecture Recordings, ClassTranscribe

Lecture Review Questions and Answers

CampusWire Discussion (sign-up link/code: 9325)

Canvas Submission (gradescope code: RV8DEW)

 

Textbook: Applied Machine Learning by David Forsyth

 

Assignments

HW 1 – Instance-based Methods (Sep 14)

HW 2 – PCA and Linear Models (Sep 28)

HW 3 – PDFs and Outliers (Oct 12)

HW 4 – Trees and MLPs (Nov 2)

HW 5 – Deep Learning and Applications (Nov 16)

Final Project (Dec 13)

Applied Machine Learning course graphic

 

 

  Class Schedule   (subject to change)

Date

Topic

Link

Reading/Notes

Aug 25 (Tues)

Introduction

ppt ; pdf

Jupyter, numpy, linear algebra tutorials ipynb

 

Fundamentals of Learning

 

 

Aug 27 (Thurs)

K-NN Classification, Data Representation

 

AML Ch 1.1-1.2

Probability/Background Review (ask to review in office hours for individual help)

ppt ; pdf ;

recording

PrairieLearn Mini-hw

Sep 1 (Tues)

K-NN Regression, Generalization

 

AML Ch 1.1-1.2

Sep 3 (Thurs)

Search and Clustering

 

AML Ch 8

Sep 8 (Tues)

Dimensionality reduction: PCA, embeddings

No in-person lecture, recording only

ppt ; pdf ;

recording

AML Ch 5, 6, 19

Sep 10 (Thurs)

Linear regression, regularization

No in-person lecture, recording only

ppt ; pdf ;

recording

 AML Ch 10-11

Sep 14 (Mon)

HW 1 (Instance-based Methods) due

 

 

Sep 15 (Tues)

Linear classifiers: logistic regression, SVM

 

AML Ch 11.3, 2.1

Sep 17 (Thurs)

Naïve Bayes Classifier

 

AML Ch 2

Sep 22 (Tues)

EM and Latent Variables

 

AML Ch 9

Sep 24 (Thurs)

Density estimation: MoG, Hists, KDE

 

AML Ch 9

Sep 28 (Mon)

HW 2 (PCA and Linear Models) due

 

 

Sep 29 (Tues)

Outliers and Robust Estimation

 

Oct 1-4

Exam 1 at CBTF

Optional review on Oct 1 in lecture

 

 

practice questions ; ref sheet

Oct 6 (Tues)

Decision Trees

 

AML Ch 2

Oct 8 (Thurs)

Ensembles and Random Forests

 

AML Ch 2

 

Deep Learning

 

 

Oct 12 (Mon)

HW 3 (PDFs and Outliers)

 

 

Oct 13 (Tues)

Stochastic Gradient Descent 

 

AML Ch 2.1; Pegasos (Shalev-Shwartz et al. 2007)

Oct 15 (Thurs)

MLPs and Backprop

 

AML 16

Oct 20 (Tues)

CNNs and Keys to Deep Learning

 

AML Ch 17-18, ResNet (He et al. 2016)

Oct 22 (Thurs)

Deep Learning Optimization and Computer Vision

 

 PyTorch Tutorial from CS444

Oct 27 (Tues)

Words and Attention

 

Sub-word Tokenization (Sennrich et al. 2016)

Word2Vec (Mikolov et al. 2013)

Attention is all you need (Vaswani et al. 2017)

Transformer tutorial/walkthrough

Oct 29 (Thurs)

Transformers in Language and Vision

 

BERT (Devlin et al. 2019)

ViT (Dosovitskiy et al. 2021)

Unified-IO (Lu et al. 2022)

Nov 2 (Mon)

HW 4 (Trees and MLPs) due

 

 

Nov 3 (Tues)

Foundation Models: CLIP and  GPT

 

CLIP (Radford et al. 2021)

Nov 5-8

Exam 2 at CBTF (Optional review on Nov 5)

 

practice questions;  ref sheet

 

Applications

 

 

Nov 10 (Tues)

Ethics and Impact of AI

 

Nov 12 (Thurs)

Bias in AI, Fair ML

 

 

Nov 16 (Mon)

HW 5 (Deep Learning and Applications) due

 

 

Nov 17 (Tues)

Building and Deploying ML

Guest speaker: Chenxi Yu (State Farm)

 

(no slides)

Nov 19 (Thurs)

Audio and 1D Signals

 

Audio Deep Learning

Nov 21-Nov 29

Fall Break

 

 

Dec 1 (Tues)

Reinforcement Learning

 

Dec 3 (Thurs)

Review, summary, looking forward

 

 

Dec 8 (Tues)

No Class – Office hours for Final Project in Lecture Hall (CIF 0027) 12:30-1:30

 

 

Dec 3-8

Exam 3 at CBTF

 

 practice questions; ref sheet

Dec 13 (Sun)

Final Project due (cannot be late)