Applied Machine Learning
(CS 441) – Fall 2025
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: 4730)
Canvas Submission (Gradescope code: 22Y75D)
Textbook: Applied Machine Learning by David Forsyth
Assignments
HW 1 – Instance-based Methods (Sep 15)HW 2 – PCA and Linear Models (Sep 29)HW 3 – PDFs and Outliers (Oct 13)HW
4 – Trees and MLPs (Nov 3)
HW 5 – Deep Learning and Applications (Nov
17)
Final
Project (Dec 14)
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Class Schedule (subject to change)
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Date |
Topic |
Link |
Reading/Notes |
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Aug 26 (Tues) |
Introduction |
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Fundamentals of Learning |
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Aug 28 (Thurs) |
K-NN Classification, Data Representation |
AML Ch 1.1-1.2 |
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Sep 2 (Tues) |
K-NN Regression, Generalization |
AML Ch 1.1-1.2 |
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Sep 4 (Thurs) |
Search and Clustering |
AML Ch 8 |
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Sep 8 (Mon) |
Probability/Background
Review (live
zoom session 9-10pm) |
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Sep 9 (Tues) |
Dimensionality reduction: PCA, embeddings |
AML Ch 5, 6, 19 |
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Sep 11 (Thurs) |
Linear regression, regularization |
AML Ch 10-11 |
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Sep 15 (Mon) |
HW 1 (Instance-based Methods) due |
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Sep 16 (Tues) |
Linear classifiers: logistic regression, SVM |
AML Ch 11.3, 2.1 |
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Sep 18 (Thurs) |
Naïve Bayes Classifier |
AML Ch 2 |
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Sep 23 (Tues) |
EM and Latent Variables |
AML Ch 9 |
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Sep 25 (Thurs) |
Density estimation: MoG, Hists, KDE |
AML Ch 9 |
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Sep 29 (Mon) |
HW 2 (PCA and Linear
Models) due |
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Sep 30 (Tues) |
Outliers and Robust
Estimation |
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Oct 2-5 |
Exam 1 at CBTF Optional review on Oct 2 in
lecture |
pdf
; |
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Oct 7 (Tues) |
Decision Trees |
AML Ch 2 |
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Oct 9 (Thurs) |
Ensembles and Random Forests |
AML Ch 2 |
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Deep Learning |
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Oct
13 (Mon) |
HW 3 (PDFs and Outliers) |
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Oct 14 (Tues) |
Stochastic Gradient Descent |
AML Ch 2.1; Pegasos (Shalev-Shwartz et al. 2007) |
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Oct 16 (Thurs) |
MLPs and Backprop |
AML 16 |
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Oct 21 (Tues) |
CNNs and Keys to Deep Learning |
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AML Ch 17-18, ResNet
(He et al. 2016) |
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Oct 23 (Thurs) |
Deep Learning Optimization and Computer Vision |
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Oct 28 (Tues) |
Words and Attention |
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Sub-word Tokenization (Sennrich et al. 2016) Word2Vec (Mikolov
et al. 2013) Attention is all you need
(Vaswani et al. 2017) |
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Oct 30 (Thurs) |
Transformers in Language and Vision |
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BERT (Devlin et al. 2019) ViT (Dosovitskiy et al. 2021) Unified-IO (Lu et al. 2022) |
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Nov 3
(Mon) |
HW 4 (Trees and MLPs) due |
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Nov 4 (Tues) |
Foundation Models: CLIP and GPT-3 |
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CLIP (Radford et al. 2021) |
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Nov 6-9 |
Exam 2 at CBTF
(Optional review on Nov 7) |
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Applications |
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Nov 11 (Tues) |
Ethics and Impact of AI |
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Nov 13 (Thurs) |
Bias in AI, Fair ML |
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Nov 14 (Fri) |
Final Project Planning Form
due |
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See CampusWire
post #207 |
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Nov 17 (Mon) |
HW 5 (Deep Learning and
Applications) due |
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Nov 18 (Tues) |
Audio and 1D Signals |
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Nov 20 (Thurs) |
Building and Deploying ML Guest speaker: Chenxi
Yu (State Farm) |
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(no slides) |
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Nov 22-Nov 30 |
Fall Break |
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Dec 2 (Tues) |
Reinforcement Learning |
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Dec 4 (Thurs) |
Review, summary, looking
forward |
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Dec 4-9 |
Exam 3 at CBTF |
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Dec 14 (Sun) |
Final Project due (cannot be late) |
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