Important Links
- Syllabus (pdf), tentative course schedule (pdf), and prerequisite quiz (ungraded)
- Brightspace (Grades) - For the first time logging into Piazza or Gradescope, please click the links in Brightspace under “Content” and then under the module “Piazza and Gradescope Links”. This will help link your Purdue account with these external learning tools. After the first time, you can just use the links below.
- Piazza (Announcements and discussion)
- Gradescope (Quizzes and assignment submission)
- 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/
- [DL] Deep Learning by Ian Goodfellow, Yoshua Bengio, and Aaron Courville, 2016. http://www.deeplearningbook.org
- [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/
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
- Week 3 - Machine learning
- Tuesday: Intro. to ML; PCA generalization demo (notebook, pdf); Optional related reading: DL, Ch. 5.1
- Thursday: 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
- Week 4 - Linear models and gradient descent
- Tuesday: Linear and Logistic Regression; Optional related reading: PY, Linear regression, DL, Ch. 5.1.4 (short), ML, Ch. 7 and Ch. 8 (in-depth)
- Thursday: Gradient Descent; Gradient descent demo (notebook, pdf)
- Week 5 - Loss functions and basics of deep learning
- Tuesday: Loss functions and regularization; Optional related reading: ML, Ch. 8 section 8.3
- Thursday: Basics of deep learning; PyTorch and automatic differentiation (notebook, pdf)
- Week 6 - Convolutional networks
- Week 7 - Natural Langauge Processing
- Tuesday: (CNNs continued)
- Thursday: Recurrent Neural Networks (RNN); Demo of character-level RNN classification (tutorial data, notebook, pdf)
- Week 8 - Natural Langauge Processing
- Tuesday: (RNNs continued); Demo of character-level RNN generation (tutorial data, notebook, pdf); Demo of vanishing and exploding gradients (notebook, pdf);
- Thursday: (RNNs continued)
- Week 9 - Natural Langauge Processing
- Tuesday: Attention and Transformers; Demo of seq-2-seq language translation (notebook, pdf)
- Thursday: (Attention and transformers continued)
- Week 10 - Review of probability
- Tuesday: Review of probability; Optional related reading: DL, Ch. 3, ML, Ch. 2
- Thursday: Guest lecture on Generative AI, Text2Image demo (notebook, pdf), Image Reward demo (notebook, pdf).
- Week 11 - Density estimation and 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 12 - Diffusion Models
- Tuesday: Diffusion Models
- Thursday: Midterm 2
- Week 13 - Reinforcement Learning
- Tuesday: (Diffusion Models continued)
- Thursday: Introduction to Reinforcement Learning; Optional reading: Chapter 1 of Reinforcement Learing: An Introduction
- Week 14 - Reinforcement Learning
- Tuesday: Multi-armed Bandits; Demo of bandit algorithms (notebook, pdf)
- Thursday: Markov Decision Processes
- Week 15 - Reinforcement Learning
- Tuesday: RL Algorithms