ECE 47300, Intro. to Artificial Intelligence (Spring 2023)

Important Links

• Syllabus (pdf), tentative course schedule (pdf), and prerequisite quiz (ungraded)
• Brightspace (Grades) - For the first time logging into Piazza and Gradescope, please click the links in Brightspace under “Content” and then under the module “Piazza, Gradescope, and Circuit 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 (Online quizzes, exams, and assignment submission)
• Google Colab (Online computing environment including GPUs)

Optional textbooks

The bracketed acronym is used for referencing these books.

1. [DL] Deep Learning by Ian Goodfellow, Yoshua Bengio, and Aaron Courville, 2016. http://www.deeplearningbook.org
2. [ML] Machine Learning: A Probabilistic Perspective by Kevin P. Murphy, 2012. https://ebookcentral.proquest.com/lib/purdue/detail.action?docID=3339490
3. [PY] Python Data Science Handbook by Jake VanderPlas, 2016. https://jakevdp.github.io/PythonDataScienceHandbook/

Lecture content by week

1. Week 1 - Introduction to artificial intelligence
   • Tuesday: Introduction to AI. See syllabus, course schedule, and course project links above.
   • Thursday: Overview of AI; Started PCA and linear algebra (see notes below)
2. Week 2 - PCA and linear algebra
   • Tuesday: Principal Components Analysis (PCA), Review of linear algebra (notebook, pdf); Related reading: DL, Ch.2
   • Thursday: See PCA notes; PCA demo (notebook, pdf )
3. Week 3 - Introduction to 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
4. 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); Loss functions and regularization; Optional related reading: ML, Ch. 8 section 8.3
5. Week 5 - Basics of deep learning
   • Tuesday: Basics of deep learning; PyTorch and automatic differentiation (notebook, pdf)
   • Thursday: Basics of convolutional neural networks (CNN); Convolutions demo (notebook, pdf); Optional related reading: DL, Ch. 9