Important Links
- Syllabus (pdf), tentative course schedule (pdf), and prerequisite quiz (ungraded)
- Brightspace (Grades)
- Piazza (Announcements and discussion)
- Gradescope (Quizzes and assignment submission) - You will need to click the link on Brightspace under “Content” and “Gradescope Link” the first time to link your account. After that you can use this link I believe.
- Google Colab (Online computing environment including GPUs)
Optional textbooks
The bracketed acronym is used for referencing these books.
- [DD] Dive into Deep Learning by Aston Zhang, Zachary C. Lipton, Mu Li, and Alexander J. Smola, 2023. https://d2l.ai/
- [PPA] Patterns, predictions, and actions: A story about machine learning by Moritz Hardt and Benjamin Recht, 2022, https://mlstory.org/pdf/patterns.pdf
- [ML] Machine Learning: A Probabilistic Perspective by Kevin P. Murphy, 2012. https://ebookcentral.proquest.com/lib/purdue/detail.action?docID=3339490
- [PY] Python Data Science Handbook by Jake VanderPlas, 2016. https://jakevdp.github.io/PythonDataScienceHandbook/
- [DL] Deep Learning by Ian Goodfellow, Yoshua Bengio, and Aaron Courville, 2016. http://www.deeplearningbook.org
Lecture content by week
- Week 1 - Introduction to artificial intelligence
- Tuesday: Introduction to AI. See syllabus, course schedule, and course project links above.
- Thursday: Principal Components Analysis (PCA), Review of linear algebra (notebook, pdf), Broadcasting rules in NumPy (and PyTorch) (notebook, pdf); Related reading: DL, Ch.2
- Week 2 - PCA
- Tuesday: (PCA continued)
- Thursday: (PCA continued), PCA demo (notebook, pdf ); Intro. to ML; PCA generalization demo (notebook, pdf); Optional related reading: DL, Ch. 5.1
- Week 3 - Machine learning
- Tuesday: (Intro to ML continued); K-nearest neighbors (KNN) and evaluating ML methods, (pdf); KNN Demo (notebook, pdf); Optional related reading: KNN Classifier Notes, DL, Ch. 5.2-5.3
- Thursday: Linear and Logistic Regression; Optional related reading: PY, Linear regression, DL, Ch. 5.1.4 (short), ML, Ch. 7 and Ch. 8 (in-depth)
- Week 4 - Gradient descent and loss functions
- Tuesday: Gradient Descent; Gradient descent demo (notebook, pdf)
- Thursday: Loss functions and regularization; Optional related reading: ML, Ch. 8 section 8.3
- Week 5 - Basics of deep learning and convolutional networks
- Tuesday: Basics of deep learning
- Thursday: (Basics of deep learning continued); PyTorch and automatic differentiation (notebook, pdf)
- Week 6 - Convolutional networks
- Week 7 - Natural Langauge Processing
- Tuesday: Recurrent Neural Networks (RNN); Demo of character-level RNN classification (tutorial data, notebook, pdf); Demo of character-level RNN generation (tutorial data, notebook, pdf); Demo of vanishing and exploding gradients (notebook, pdf)
- Thursday: Midterm 1
- Week 8 - Natural Langauge Processing
- Tuesday: (RNNs continued); Attention and Transformers; Demo of seq-2-seq language translation (notebook, pdf)
- Thursday: (Transformers continued)
- Week 9 - Review of probability
- Tuesday: (Transformers continued); Review of probability; Optional related reading: DL, Ch. 3, ML, Ch. 2
- Thursday: (Review of probability continued)
- Week 10 - Autoencoders
- Tuesday: Density Estimation
- Thursday: Autoencoders and VAEs; Optional related reading: Introduction to VAEs by original authors (2019), Original VAE paper (2013), From Variational to Deterministic Autoencoders
- Week 11 - Diffusion models
- Tuesday: (Autoencoders continued)
- Thursday: Diffusion Models
- Week 12 - Reinforcement Learning
- Tuesday: Introduction to Reinforcement Learning; Optional reading: Chapter 1 of Reinforcement Learing: An Introduction
- Thursday: Midterm 2
- Week 13 - Reinforcement Learning
- Tuesday: Multi-armed Bandits; Demo of bandit algorithms (notebook, pdf)