Schedule (Fall 2024)

Lectures

• Introduction to CSC 461 [Slides]

  • Readings: Google's Python Class [WEB], Numpy Tutorial [WEB]

• Machine Learning Basics [Slides]

  • Readings: Introduction (DL Book) [PDF]

• Math/Python Preliminaries [Slides]

  • Readings: Linear Algebra (DL Book) [PDF], Numpy Broadcasting [WEB]

• Clustering, K-Means [Slides] [Colab]

  • Readings: Chapter 4 [PDF]

• Hierarchical Clustering [Slides] [Colab]

  • Readings: Section 22.1 [PDF]

• PCA [Slides] [Colab 1] [Colab 2]

  • Readings: Section 23.1 [PDF]

• Supervised Learning [Slides]

  • Readings: Chapters 2 and 3 [PDF]

• k-Nearest Neighbors [Slides]

  • Readings: Chapter 19 [PDF]

• Model Selection [Slides] [Colab]

  • Readings: Chapter 11 [PDF]

• Decision Trees I [Slides]

  • Readings: Chapter 18 [PDF]

• Decision Trees II [Slides] [Colab]

topics for midterm exam end here

• Bagging [Slides]

• Boosting [Slides] [Colab]

• Linear Regression [Slides] [Colab]

• Logistic Regression I [Slides]

• Logistic Regression II [Slides]

• Gradient Descent [Slides] [Colab] [Colab]

• Gradient Descent in Machine Learning [Slides] [Colab 1] [Colab 2]

• Feature Transformation [Slides] [Colab]

• Regularization [Slides] [Colab]

• Multinomial Logistic Regression [Slides] [Colab]

• Automatic Differentiation [Slides] [Colab]

• Neural Networks [Slides] [Colab]

• Convolutional Neural Networks [Slides] [Colab]

• Training Neural Networks [Slides]

• Recurrent Neural Networks, LSTMs [Slides]

Suggested readings for final exam

• Ensemble Methods [PDF]
• Chapter 2 (UFML book) [PDF]
• Chapter 5 (SLP book) [PDF]
• Chapter 7 (SLP book) [PDF]
• Chapter 9 (DL Book) [PDF]

Homework Assignments

• Assignment 1, due Sep 16th 11:59p
• Assignment 2, due Oct 1st 11:59p
• Assignment 3, due Nov 1st 11:59p
• Assignment 4, due Dec 1st 11:59p

Exams

• Midterm Exam, Oct 11, 2-4pm, TBA
• Final Exam, Dec 16, 3-5pm, TBA

Project

• Final Projects [PDF]