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 |
|---|---|
| 01/13 | Course overview, Introduction to ML |
| 01/15 | k-Nearest Neighbors |
| No Class (Martin Luther King Jr. Holiday) | |
| 01/22 | Scikit-learn Workflow |
| 01/27 | Naïve Bayes classifier |
| 01/29 | Decision Trees |
| 02/03 | Logistic Regression |
| 02/05 | Linear Regression |
| 02/10 | Evaluation Metrics |
| 02/12 | Data Preprocessing |
| 02/17 | Model Evaluation, Hyperparameter Tuning |
| 02/19 | Support Vector Machines (SVM) |
| 02/24 | Unsupervised Learning, K-Means |
| 02/26 | Dimensionality Reduction |
| 03/03 | Ensemble and Boosting |
| 03/05 | Midterm 1 Exam |
| No Class (Spring Break) | |
| No Class (Spring Break) | |
| 03/17 | Deep Learning Introduction |
| 03/19 | MLP, Backpropagation |
| 03/24 | Convolutional Neural Networks |
| 03/26 | Recurrent Neural Networks |
| 03/31 | Transformers |
| 04/02 | Reinforcement Learning |
| 04/07 | Midterm 2 Exam |
| 04/09 | Large Language Models |
| 04/14 | Graph Neural Networks |
| 04/16 | Project 2 Showcase |
| 04/21 | Project 3 Showcase |
| 04/23 | Course Conclusion |