CS:4980 Introduction to Machine LearningComputer Science, College of Liberal Arts & Sciences, University of IowaInstructor: Tianbao Yang Email: [first-name]-[last-name] at uiowa.edu Office: 101E MacLean Hall Office hours: 1:20pm - 2:20pm Monday, Wednesday, Friday or by appointment |
| Week | Date | Topic | Note |
|---|---|---|---|
| Mon-Aug. 25 | Introduction | ||
| Week 1 | Wed-Aug. 27 | Review: Linear Algebra | |
| Fri-Aug. 29 | Review: Probability Theory | ||
| Mon-Sept. 1 | No class | Holiday | |
| Week 2 | Wed-Sept. 3 | Linear Regression | Ch. 3 |
| Fri-Sept. 5 | Linear Regression | ||
| Mon-Sept. 8 | Linear Regression | ||
| Week 3 | Wed-Sept. 10 | Linear Classification (KNN) | Homework 1 due |
| Fri-Sept. 12 | Linear Classification (KNN) | Ch. 2 | |
| Mon-Sept. 15 | Linear Classification (Generative Models) | ||
| Week 4 | Wed-Sept. 17 | Linear Classification (Discriminant) | |
| Fri-Sept. 19 | Linear Classification (Discriminative Models) | Ch. 4 | |
| Mon-Sept. 22 | Review of Convex Optimization | "Convex Optimization" by Stephen Boyd & Lieven Vandenberghe | |
| Week 5 | Wed-Sept. 24 | Review of Convex Optimization | Homework 2 due |
| Fri-Sept. 26 | Linear Classification (SVM) | ||
| Mon-Sept. 29 | Liner Classification (SVM) | ||
| Week 6 | Wed-Oct. 1 | Kernel Methods | |
| Fri-Oct. 3 | Kernel Methods | Ch. 6 & Ch. 7 & Ch. 12 | |
| Mon-Oct. 6 | Clustering | ||
| Week 7 | Wed-Oct. 8 | Clustering | Homework 3 due |
| Fri-Oct. 10 | Clustering | Ch. 9 | |
| Mon-Oct. 13 | Semi-supervised Learning | ||
| Week 8 | Wed-Oct. 15 | Semi-supervised Learning | |
| Fri-Oct. 17 | Semi-supervised Learning | SSL Survey | |
| Mon-Oct. 20 | Model Boosting and Bagging | ||
| Week 9 | Wed-Oct. 22 | Model Boosting and Bagging | Homework 4 due |
| Fri-Oct. 24 | Model Boosting and Bagging | Ch. 14 | |
| Mon-Oct. 27 | Bayesian Learning | ||
| Week 10 | Wed-Oct. 29 | Bayesian Learning | |
| Fri-Oct. 31 | Bayesian Learning | Ch. 3&4&6 | |
| Mon-Nov. 3 | Bayesian Learning | ||
| Week 11 | Wed-Nov. 5 | Bayesian Learning | Homework 5 due |
| Fri-Nov. 7 | Selected Topics: Online Learning | Online Learning: book "prediction, learning and games" | |
| Mon-Nov. 10 | Selected Topics: Stochastic Optimization for Big Data | Stochastic Optimization Tutorial | |
| Week 12 | Wed-Nov. 12 | Selected Topics: Stochastic Optimization for Big Data | |
| Fri-Nov. 14 | Selected Topics: Distance Metric Learning | Distance Metric Learning Tutorial | |
| Mon-Nov. 17 | Selected Topics: Distance Metric Learning | ||
| Week 13 | Wed-Nov. 19 | Recent Advances in Big Data: Deep Learning | Homework 6 due |
| Fri-Nov. 21 | Recent Advances in Big Data: Deep Learning | Deep Learning Tutorial | |
| Mon-Nov. 24 | No class | Holiday | |
| Week 14 | Wed-Nov. 26 | No class | Holiday |
| Fri-Nov. 28 | No class | Holiday | |
| Mon-Dec. 1 | Presentation | ||
| Week 15 | Wed-Dec. 3 | Presentation | |
| Fri-Dec. 5 | Presentation | ||
| Mon-Dec. 8 | Presentation | ||
| Week 16 | Wed-Dec. 10 | Presentation | |
| Fri-Dec. 12 | Presentation | ||
| Mon-Dec. 15 | No class | Exam week | |
| Week 17 | Wed- Dec. 17 | No class | Exam Week |
| Fri- Dec. 21 | No class | Project Report Due |