Schedule
The schedule is subject to change. Check back often. Notes/codes/slides links are placeholders for future lectures. They will be updated no later than the lecture date.
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EventDateDescriptionCourse Material
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Assignment09/03/2025
WednesdayHomework 1 released! -
Lecture09/03/2025
Wednesday01 - Deep Learning Concepts and Course LogisticsReadings:
- Understanding Deep Learning, Chapter 1
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Assignment09/08/2025
MondayHomework 2 released! -
Lecture09/08/2025
Monday02 - Supervised LearningReadings:
- Understanding Deep Learning, Chapter 2
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Discussion09/10/2025
WednesdayDiscussion 1Notebook: 00_pytorch.ipynb
Suggested Readings: -
Lecture09/10/2025
Wednesday03 - Loss FunctionsReadings:
- Understanding Deep Learning, Chapter 5
- Mathematics for Machine Learning, Chapters 6 and 8, especially 8.1.3
- Maximum Likelihood Estimation Examples from an MIT course. This video walks through MLE examples for binomial and normal distributions, but does not pull out equivalent loss functions like we did in lecture.
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Due09/10/2025 23:59
WednesdayHomework 1 due -
Assignment09/15/2025
MondayHomework 3 released! -
Lecture09/15/2025
Monday04 - Gradient DescentReadings:
- Understanding Deep Learning, Chapter 3
- Mathematics for Machine Learning, Chapter 7
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Discussion09/17/2025
WednesdayDiscussion 2Notebook: 02_gradient_descent.ipynb
Suggested Readings: -
Lecture09/17/2025
Wednesday05 - Shallow Networks[slides]Readings:
- Understanding Deep Learning, Chapter 3
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Due09/17/2025 23:59
WednesdayHomework 2 due -
Lecture09/22/2025
Monday06 - Shared Compute Cluster TutorialReadings:
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Discussion09/24/2025
WednesdayDiscussion 3Notebook: 03_scc.ipynb
Suggested Readings: -
Lecture09/24/2025
Wednesday07 - Deep Networks[slides]Readings:
- Understanding Deep Learning, Chapter 4
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Due09/24/2025 23:59
WednesdayHomework 3 due -
Lecture09/29/2025
Monday08 - Fitting Models[slides]Readings:
- Understanding Deep Learning, Chapter 6
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Discussion10/01/2025
WednesdayDiscussion 4Notebook: 04_neural_network.ipynb
Suggested Readings: -
Lecture10/01/2025
Wednesday09 - Backpropagation and Initialization[slides]Readings:
- Understanding Deep Learning, Chapters 7.1 - 7.6
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Lecture10/06/2025
Monday10 - Measuring Performance[slides]Readings:
- Understanding Deep Learning, Chapter 8
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Discussion10/08/2025
WednesdayDiscussion 5Notebook: 05_deep.ipynb
Suggested Readings:- TBD
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Lecture10/08/2025
Wednesday11 - Regularization[slides]Readings:
- Understanding Deep Learning, Chapter 9
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Lecture10/14/2025
Tuesday12 - Convolutional Neural Networks[slides]Readings:
- Understanding Deep Learning, Chapter 10
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Discussion10/15/2025
WednesdayDiscussion 6Notebook: 06_regularization.ipynb
Suggested Readings:- TBD
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Lecture10/15/2025
Wednesday13 - Residual Networks[slides]Readings:
- Understanding Deep Learning, Chapter 11
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Session Ends10/18/2025 04:59
SaturdayFirst 7 week session ends -
Session Begins10/20/2025 05:00
Monday2nd 7 week session begins -
Lecture10/20/2025
Monday14 - Recurrent Neural Networks[slides]Readings:
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Discussion10/22/2025
WednesdayDiscussion 7Notebook: 07_rnn.ipynb
Suggested Readings:- TBD
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Lecture10/22/2025
Wednesday15 - Transformers Part 1[slides]Readings:
- Understanding Deep Learning, Chapter 12
- Optional: The Illustrated Transformer
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Lecture10/27/2025
Monday16 - Transformers Part 2[slides]Readings:
- Understanding Deep Learning, Chapter 12
- Optional The Illustrated Transformer
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Discussion10/29/2025
WednesdayDiscussion 8Notebook: 08_attention.ipynb
Suggested Readings:- TBD
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Lecture10/29/2025
Wednesday17 - Vision & Multimodal Transformers[slides]Readings:
- See slides for references
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Lecture11/03/2025
Monday18 - Training, Tuning and Evaluating LLMs[slides]Readings:
- See slides for references
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Discussion11/05/2025
WednesdayDiscussion 9Notebook: 09_llms.ipynb
Suggested Readings:- TBD
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Lecture11/05/2025
WednesdayContrastive Learning[slides] -
Lecture11/10/2025
Monday20 - Adversarial Inputs and Generative Adversarial Models[slides]Readings:
- Understanding Deep Learning, Chapters 14 and 15
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Discussion11/12/2025
WednesdayDiscussion 10Notebook: 10_adversarial.ipynb
Suggested Readings:- TBD
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Lecture11/12/2025
Wednesday21 - Unsupervised Learning and Variational Autoencoders[slides]Readings:
- Understanding Variational Autoencoders
- Understanding Deep Learning, Chapter 17 (optional)
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Lecture11/17/2025
Monday22 - Diffusion Models[slides]Readings:
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Discussion11/19/2025
WednesdayDiscussion 11Notebook: 11_diffusion.ipynb
Suggested Readings:- TBD
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Lecture11/19/2025
Wednesday23 - Graph Neural Networks[slides]Readings:
- Understanding Deep Learning, Chapter 13
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Lecture11/24/2025
Monday24 - Using Pre-Trained Models[slides]Readings:
- TBD
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Start of Thanksgiving Recess11/26/2025 05:00
WednesdayThanksgiving recess begins -- Have a great break! -
End of Thanksgiving Recess12/01/2025 04:59
MondayThanksgiving recess ends -
Lecture12/01/2025
Monday25 - Scaling Concerns[slides]Readings:
- TBD
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Discussion12/03/2025
WednesdayDiscussion 12Notebook: 12_finetuning.ipynb
Suggested Readings:- TBD
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Lecture12/03/2025
Wednesday26 - Reasoning and World Models[slides]Readings:
- TBD
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Lecture12/08/2025
Monday27 - Object Detection and Segmentation[slides]Readings:
- TBD
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Discussion12/10/2025
WednesdayDiscussion 13Notebook: 13_reasoning.ipynb
Suggested Readings:- TBD
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Lecture12/10/2025
Wednesday28 - Benchmarks[slides]Readings:
- TBD