Max Han
CSDS340: Introduction to Machine Learning
Xiaotian (Max) Han
Spring 2025, M/W 3:20–4:35 PM
Course Description
This course provides an in-depth introduction to machine learning (ML) algorithms and their practical implementation. We will examine key learning settings, explore diverse algorithms, and learn how to implement and evaluate their performance. We will also discuss handling noise, missing values, and scalability challenges, as well as review common ML tools and libraries.
Grading Policy
- Homework: 20% (5% each)
- Midterm 1: 15%
- Midterm 2: 20%
- Project 1: 15%
- Project 2: 15%
- Project 3: 15%
- Course evaluation: 5% extra credit
Late Policy: Up to 1 hour late: 100% of your final score; 1–24 hours late: 90% of your final score; 24–48 hours late: 80% of your final score; Over 48 hours late: 0% of your final score.
Course Materials
Textbook: - Machine Learning with PyTorch and Scikit- Learn, S. Raschka, Y. H. Liu, and V. Mirjalili, 2022. Other Materials - The Elements of Statistical Learning, 2nd, Trevor Hastie,Robert Tibshirani, Jerome Friedman - Machine Learning: A Probabilistic Perspective, Kevin P. Murphy, 2012 - The Little Book of Deep Learning, François Fleuret, May 19th, 2024. - Dive into Deep Learning, Aston Zhang, Zachary C. Lipton, Mu Li, Alexander J. Smola
Course Schedule
The course schedule is subject to minor change.
| Date | Topic | Homework |
|---|---|---|
| 01/13 | Course overview, Introduction to ML | |
| 01/15 | K-nearest neighbors | Homework 1 out |
| No Class (Martin Luther King Jr. Holiday) | ||
| 01/22 | Naïve Bayes classifier | Homework 2 out |
| 01/27 | Decision Trees | Homework 3 out |
| 02/05 | Logistic Regression | Homework 4 out |
| 02/10 | Linear Regression | |
| 02/03 | Support Vector Machines (SVM) | Homework 1 due |
| 02/19 | Supervised/unsupervised learning | |
| 02/24 | Clustering, k-means clustering | Homework 2 due |
| 02/26 | Dimensionality reduction, PCA | |
| 01/29 | Ensemble Learning, Random Forests | Homework 3 due |
| 02/05 | Kernel SVM | |
| 02/19 | Regression, and classification | Homework 4 due |
| 02/12 | Model evaluation, Hyperparameter tuning | |
| 02/17 | Boosting classifiers | |
| 02/19 | Homeworks Showcase | Project 1 out |
| 03/03 | Midterm 1 Exam | |
| 03/05 | Introduction to deep learning | |
| No Class (Spring Break) | ||
| No Class (Spring Break) | ||
| 03/17 | Introduction to deep learning | |
| 03/19 | Backpropagation | Project 1 due |
| 03/24 | Convoluation Neural Networks | Project 2 out |
| 03/26 | Recurrent Neural Networks | |
| 03/31 | Transformer | Project 3 out |
| 04/02 | Project 1 Showcase | |
| 04/07 | Midterm 2 Exam | |
| 04/09 | Large Language Model | |
| 04/14 | Graph Neural Network | Project 2 due |
| 04/16 | Autoencoder | |
| 04/21 | Project 2 Showcase | Project 3 due |
| 04/23 | Reinforcement learning | |
| 04/28 | Project 3 Showcase |