Course Overview

This course provides an introduction to machine learning with a special focus on engineering applications. The course starts with a mathematical background required for machine learning and covers approaches for supervised learning (linear models, kernel methods, decision trees, neural networks) and unsupervised learning (clustering, dimensionality reduction), as well as theoretical foundations of machine learning (learning theory, optimization). Evaluation will consist of mathematical problem sets and programming projects targeting real-world engineering applications.


This course is intended for graduate students and qualified undergraduate students with a strong mathematical and programming background. Undergraduate level training or coursework in algorithms, linear algebra, calculus, probability, and statistics is suggested. A background in programming will also be necessary for the problem sets; students are expected to be familiar with python or learn it during the course. At CMU, this course is most similar to MLD's 10-601 or 10-701, though this course is meant specifically for students in engineering.


There will be no required textbooks, though we suggest the following to help you to study (all available online): We will provide suggested readings from these books in the schedule below.


We will use Piazza for class discussions. Please go to the course Piazza site to join the course forum (note: you must use a email account to join the forum). We strongly encourage students to post on this forum rather than emailing the course staff directly (this will be more efficient for both students and staff). Students should use Piazza to:

The course Academic Integrity Policy must be followed on the message boards at all times. Do not post or request homework solutions! Also, please be polite.

Staff Contact Info


Prof. Carlee Joe-Wong


Sundar Anand
Courage Ekoh
Charlie Hou
Yafei Hu
Tianshu Huang
Yu-Jhe Li
Ching Yi lin
I-Cheng Lin
Janvier Nshimyumukiza
Jace Rozsa
Roshan Sharma
Carolene Siga

Grading Policy

Grades will be based on the following components:

Gradescope: We will use Gradescope to collect PDF submissions of each problem set. Upon uploading your PDF, Gradescope will ask you to identify which page(s) contains your solution for each problem – this is a great way to double check that you haven’t left anything out. The course staff will manually grade your submission, and you’ll receive feedback explaining your final marks.

Regrade Requests: If you believe an error was made during grading, you’ll be able to submit a regrade request on Gradescope. For each homework, regrade requests will be open for only 1 week after the grades have been published. This is to encourage you to check the feedback you’ve received early!

Academic Integrity Policy

Group studying and collaborating on problem sets are encouraged, as working together is a great way to understand new material. Students are free to discuss the homework problems with anyone under the following conditions: Students are encouraged to read CMU's Policy on Cheating and Plagiarism.

Using LaTeX

Students are strongly encouraged to use LaTeX for problem sets. LaTeX makes it simple to typeset mathematical equations and is extremely useful for graduate students to know. Most of the academic papers you read were written with LaTeX, and probably most of the textbooks too. Here is an excellent LaTeX tutorial and here are instructions for installing LaTeX on your machine.


This course is based in part on material developed by Fei Sha, Ameet Talwalkar, Matt Gormley, and Emily Fox. We also thank Anit Sahu and Joao Saude for their help with course development.

Schedule (Subject to Change)

DateTopics ReadingHW
1/19 Introduction & Math Quiz [Slides] KM, Ch.1
1/21 Recitation
1/24 MLE/MAP, Linear Algera Review [Slides] TM, Estimating Probabilities
KM, Ch. 2 (for a refresh in probability)
Math4ML (review/refresher)
Vectors, Matrices, and Least Squares
Matrix Cookbook

HW 1 released
1/26 Linear Regression, Part I [Slides] KM, Ch. 7.1-7.3
Deep Learning Book, Ch. 5*

1/28 Recitation
1/31 Linear Regression, Part II [Slides] KM, Ch. 7.4-7.6
Intro to regression

HW 2 released
HW 2 code and data
2/2 Python Overview [Slides] [Notebook]
2/4 Recitation
2/7 Overfitting, Bias/variance Trade-off, Evaluation [Slides] Deep Learning, Ch. 5.2-5.4
KM, Ch. 6.4

HW 1 due
2/9 Naive Bayes [Slides] CIML, Ch. 9
KM, Ch. 3.5

2/11 Recitation
2/14 Logistic Regression [Slides] KM, Ch. 8.1-8.4, 8.6
Discriminative vs. Generative

HW 2 due
HW 3 released
2/16 Multi-class Classification [Slides] KM, Ch. 8.5
2/18 Recitation
2/21 SVM, Part I [Slides] ESL, Ch. 12
KM Ch. 14.5

2/23 SVM, Part II [Slides] Idiot's Guide to SVM
Kernel Methods

2/25 Recitation
HW 3 due
2/28 SVM, Part III [Slides] Duality Supplement

3/2 Midterm Exam
3/14 Nearest Neighbors [Slides] CIML, Ch. 3.1-3.2
HW 4 released
3/16 Decision Trees [Slides] CIML, Ch. 1.3
KM, Ch. 16.2
ESL, Ch. 9.2

3/18 Recitation
3/21 Boosting, random forests [Slides] ESL, Ch. 10.1, 10.4-10.6
3/23 Neural Networks, Part I [Slides] Learning Deep Architectures for AI

3/25 Recitation
3/28 Neural Networks, Part II [Slides] Neural Networks and Deep Learning, Ch.3
Regularization for Deep Learning

HW 4 due
HW 5 released
3/30 Introduction to PyTorch
[Slides] [Notebook] [Pytorch Example]

4/1 Recitation
4/4 Clustering, Part I [Slides] CIML, Ch. 15.1
4/6 Clustering, Part II [Slides] ESL, Ch. 14.3.1-14.3.9
4/11 Dimensionality Reduction [Slides] PCA
Independent Component Analysis

HW 5 due
HW 6 released
4/13 Graphical Models, Part I [Slides] MJ, Ch. 2, 3, 12
4/15 Recitation
4/18 Graphical Models, Part II [Slides]
HW 7 released
4/20 Online and Reinforcement Learning [Slides]
4/22 Recitation
4/25 Special Lecture: Current Topics in ML [Slides]
HW 6 due
4/27 Last Lecture: Course Review [Slides]
4/29 Recitation HW 7 due (extended to Sunday, May 1)
5/6 Final Exam (12-3pm ET)