2025-Winter-CSE151A-Introduction to Machine Learning
Undergraduate Class, CSE, UCSD, 2025
Class Time: Tuesdays and Thursdays, 2 to 3:20 PM. Room: MOS 0113. Piazza: piazza.com/ucsd/winter2025/cse151a.
Online Lecturing for First Week
To offer waitlist students opportunities to learn more about this course, in the first week, we deliver the lecture over Zoom: https://ucsd.zoom.us/j/93540989128. The lectures will be recorded.
Overview
This course mainly focuses on introducing machine learning methods and models that are useful in analyzing real-world data. It will cover classical regression & classification models, clustering methods, and deep neural networks. No previous background in machine learning is required, but all participants should be comfortable with programming, and with basic optimization and linear algebra.
There is no textbook required, but here are some recommended readings:
- The Elements of Statistical Learning by Trevor Hastie, Robert Tibshirani, and Jerome Friedman.
- Data Mining: Concepts and Techniques by Jiawei Han et al.
- Pattern Recognition and Machine Learning by Christopher M. Bishop.
- Dive into Deep Learning book by Aston Zhang et al.
Prerequisites
Ability to code in Python: functions, control structures, string handling, arrays and dictionaries.
Familiarity with basic probability, at the level of CSE 21 or CSE 103.
Familiarity with basic linear algebra, at the level of Math 18 or Math 20F.
TAs and Tutors
- Teaching Assistants:
- Bill Hogan (whogan AT ucsd.edu)
- Zi Lin (zil061 AT ucsd.edu)
- Dheeraj Mekala (dmekala AT ucsd.edu)
- Yufan Zhuang (y5zhuang AT ucsd.edu)
- Tutor:
- Yuelei Li (yul189 AT ucsd.edu)
Office Hours
- Jingbo Shang
- Office Hour: Wednesdays, 9 to 11 AM
- Zoom link: https://ucsd.zoom.us/my/jshang
- Bill Hogan
- Office Hour: TBD
- Zi Lin
- Office Hour: TBD
- Dheeraj Mekala
- Office Hour: TBD
- Yufan Zhuang
- Office Hour: TBD
Note: all times are in Pacific Time.
Grading
- Homework: 15% each. Your lowest (of five) homework grades is dropped (or one homework can be skipped).
- Midterm: 40%.
- You should complete all work individually.
- Late submissions are NOT accepted.
Lecture Schedule
Recording Note: Please download the recording video for the full length. Dropbox website will only show you the first one hour.
HW Note: All HWs due before the lecture time 9:30 AM PT in the morning.
(the schedule is tentative)
| Week | Date | Topic & Slides | Events |
| 1 | 01/07 (Tue) | Introduction: Concepts and Evaluations | HW1 out |
| 1 | 01/09 (Thu) | A Geometric View of Linear Algebra | |
| 2 | 01/14 (Tue) | Nearest Neighbor Classification | HW1 due, HW2 out |
| 2 | 01/16 (Thu) | Gradients and Optimization | |
| 3 | 01/21 (Tue) | Least-Squares Regression, Logistic Regression, and Perceptron | |
| 3 | 01/23 (Thu) | Overfitting and Regularization | |
| 4 | 01/28 (Tue) | Support Vector Machine (SVM) | |
| 4 | 01/30 (Thu) | SVM: Duality and Kernel | HW2 due, HW3 out |
| 5 | 02/04 (Tue) | K-Means Clustering & its Variants | |
| 5 | 02/06 (Thu) | “Soft” Clustering: Gaussian Mixture | |
| 6 | 02/11 (Tue) | Principle Component Analysis | |
| 6 | 02/13 (Thu) | Midterm (no class, take-home, 24-hour) | |
| 7 | 02/18 (Tue) | Naive Bayes and Decision Tree | HW4 out |
| 7 | 02/20 (Thu) | Ensemble Learning: Bagging and Boosting | HW3 due |
| 8 | 02/25 (Tue) | Multi-class Classification | |
| 8 | 02/27 (Thu) | Feed-forward Neural Networks | |
| 9 | 03/03 (Tue) | Convolutional Neural Networks | HW4 due, HW5 out |
| 9 | 03/05 (Thu) | Semi-supervised Learning | |
| 10 | 03/10 (Tue) | Weakly-supervised Learning | |
| 10 | 03/12 (Thu) | Bias-Variance in Deep Neural Networks | HW5 due |
Homework (60%)
Your lowest (of five) homework grades is dropped (or one homework can be skipped).
- HW1: Concepts and Evaluations (15%). This homework mainly focuses on the machine learning concepts and how to evaluate different tasks.
- HW2: KNN and Linear Models (15%). This homework mainly focuses on nearest neighbor, least-square regression, logistic regression, and regularization.
- HW3: SVM and Clustering (15%). This homework mainly focuses on support vector machine, k-means, Gaussian Mixture, and PCA.
- HW4: Ensemble Learning (15%). This homework mainly focuses on decision tree, random forest, and AdaBoost.
- HW5: Neural Networks (15%). This homework mainly focuses on implementation of some simple neural networks.
Midterm (40%)
It is an open-book, take-home exam, which covers all lectures given before the Midterm. Most of the questions will be open-ended. Some of them might be slightly more difficult than homework. You will have 24 hours to complete the midterm, which is expected for about 2 hours.
- Start: Feb 13, 2:00 PM PT
- End: Feb 14, 2:00 PM PT
- Midterm problems download: TBD
- Please make your submissions on Gradescope.