ECE 50024: Machine Learning

Fall 2025

Date: Aug 25, 2025 – Dec 12, 2025

In Fall 2025, we offer the following sections, simultaneously.

• ECE 50024-EPE (CRN 21451). Online (Purdue Online). Asynchronous

Course Description

An introductory course to machine learning, with a focus on supervised learning using linear models.

• Regression. Linear regression, loss function, optimization, gradient descent, stochastic gradient descent, least squares fitting, robust regression, ridge regularization, LASSO regularization

• Linear Classifiers. Linear discriminant analysis, separating hyperplane, multi-class classification, Bayesian decision rule, geometry of Bayesian decision rule, linear regression, logistic regression, robustness of classifiers, adversarial learning

• Generative Models. Variational autoencoder, denoising diffusion probabilistic models, score matching Langevin dynamics

• Learning Theory. Bias and variance, training and testing, generalization, PAC framework, Hoeffding inequality, VC dimension.

Pre-requisites

• Purdue ECE 302 Probability

• Purdue MA 265 Linear Algebra

• Purdue ECE 647 (Optional) Convex and Stochastic Optimization and Applications

Useful Background Materials

• • Purdue ECE 302 Probability https://engineering.purdue.edu/ChanGroup/eBook.html

  • MIT 18.06 Linear Algebra: http://math.mit.edu/~gs/linearalgebra/

  • MIT 6.079 Optimization https://ocw.mit.edu/courses/6-079-introduction-to-convex-optimization-fall-2009/

  • MIT 6.012 Probability: https://www.amazon.com/Introduction-Probability-2nd-Dimitri-Bertsekas/dp/188652923X

  • Stanford EE 364A Convex Optimization: https://stanford.edu/~boyd/cvxbook/

  • Stanford ENGR 108 Linear Algebra: https://web.stanford.edu/~boyd/vmls/

Grades

• Test (80%)

  • There will be four tests. All tests are independent and not cumulative.

  • Test 1 (20%): 45-minute test during lecture. Topics for Week 1 - Week 4.

  • Test 2 (20%): 45-minute test during lecture. Topics for Week 5 - Week 8.

  • Test 3 (20%): 45-minute test during lecture. Topics for Week 9 - Week 12.

  • Test 4 (20%): 45-minute test during lecture. Topics for Week 13 - Week 16.

  • All tests are closed-book, closed-note.

• Quiz (20%)

  • Throughout the semester.

  • (For Online students only) Complete on gradescope

  • No more than 10 minutes each.

  • Most questions are short, simple, easy.

• Homework (0%)

  • Homework will be assigned but not collected.

  • Solution will be distributed.

• Final Exam (0%)

  • There is no final exam.

• Project (0%)

  • There is no project.

• Curve

  • We will curve the class.

  • Undergrad and grad will use the same curve. (This is required by the undergrad and grad committee, because this is a 500-level class.)

  • Roughly speaking, and subject to curves:

    • A: > 80

    • B: 70-80

    • C: 50-70

    • F: < 50

Textbook and References

There is no official textbook for this course. Please refer to the lecture note section of the website for our lecture materials.

A few good reference books for this course are:

• Introduction to Probability for Data Science, by Stanley Chan, draft. 1st edition. 2020.

• Pattern Classification, by Duda, Hart and Stork, Wiley-Interscience; 2 edition, 2000.

• Learning from Data, by Abu-Mostafa, Magdon-Ismail and Lin, AMLBook, 2012.

• Elements of Statistical Learning, by Hastie, Tibshirani and Friedman, Springer, 2 edition, 2009.

• Pattern Recognition and Machine Learning, by Bishop, Springer, 2006.
  
  
  

Programming

We will be primarily using Python. As such, I expect you to have elementary programming skills, e.g., writing a hello world program. More information and resources on how to use Python can be found in the programming section of this website. I found Google Colab a fairly easy-to-use platform for Python programming. You can check this out.

Besides Python, we use optimization packages to solve optimization problems. Of particular importance is CVX.