2021-Spring-CSE151A-Introduction to Machine Learning

Undergraduate Class, CSE, UCSD, 2021

Class Time: Tuesdays and Thursdays, 9:30AM to 10:50AM. Room: https://ucsd.zoom.us/j/93540989128. Piazza: https://piazza.com/class/kmmklfc6n0a32h.

Online Lecturing

Due to the COVID-19, this course will be delivered over Zoom: https://ucsd.zoom.us/j/93540989128


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:


  1. Ability to code in Python: functions, control structures, string handling, arrays and dictionaries.

  2. Familiarity with basic probability, at the level of CSE 21 or CSE 103.

  3. Familiarity with basic linear algebra, at the level of Math 18 or Math 20F.

TAs and Tutors

  • Teaching Assistants:
    • Dheeraj Mekala (dmekala AT ucsd.edu)
    • Xinghan Wang (x2wang AT ucsd.edu)
    • Weijian Xu (wex041 AT ucsd.edu)
  • Tutors:
    • Zhenyu Bi (z1bi AT ucsd.edu)
    • Yilun Hao (yih301 AT ucsd.edu)
    • Joey Hou (z9hou AT ucsd.edu)
    • Colin Wang (ziw029 AT ucsd.edu)

Office Hours

Note: all times are in Pacific Time.


  • 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)

WeekDateTopic & SlidesEvents
103/30 (Tue)Introduction: Concepts and EvaluationsHW1 out
104/01 (Thu)A Geometric View of Linear Algebra 
204/06 (Tue)Nearest Neighbor ClassificationHW1 due, HW2 out
204/08 (Thu)Gradients and Optimization 
304/13 (Tue)Least-Squares Regression, Logistic Regression, and Perceptron 
304/15 (Thu)Overfitting and Regularization 
404/20 (Tue)Support Vector Machine (SVM) 
404/22 (Thu)SVM: Duality and KernelHW2 due, HW3 out
504/27 (Tue)K-Means Clustering & its Variants 
504/29 (Thu)“Soft” Clustering: Gaussian Mixture 
605/04 (Tue)Principle Component Analysis 
605/06 (Thu)Midterm (no class, take-home, 24-hour) 
705/11 (Tue)Naive Bayes and Decision TreeHW3 due, HW4 out
705/13 (Thu)Ensemble Learning: Bagging and Boosting 
805/18 (Tue)Multi-class Classification 
805/20 (Thu)Feed-forward Neural Networks 
905/25 (Tue)Convolutional Neural NetworksHW4 due, HW5 out
905/27 (Thu)Semi-supervised Learning 
1006/01 (Tue)Weakly-supervised Learning 
1006/03 (Thu)Bias-Variance in Deep Neural NetworksHW5 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: May 6, 9:30 AM PT
  • End: May 7, 9:30 AM PT
  • Midterm problems download: here
  • Please make your submissions on Gradescope.