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 10-701, 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 use Piazza for class discussions. Please go to this Piazza website 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.

Grading Policy

Grades will be based on the following components:

Staff Contact


  Jacob Hoffman (Pitt)
  Samarth Gupta (Pitt)
  Ritwick Chaudhry (Pitt)
  Shreyas Chaudhari (Pitt)
  Soham Deshmukh (Pitt)
  Mike Weber (Pitt)
  TJ Kim(Pitt)
  Sweta Hari Kumar (Pitt)

Collaboration 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.


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.

Tentative Schedule

Date Lecture Readings Announcements
Monday, 13th Jan Lecture 1 : Intro & Math Quiz [Slides]
  • KM, Ch. 1.

Wed, 15th Jan Lecture 2 : Probability Review, MLE/MAP [Slides]
Fri, 17th Jan Recitation

HW 1 released HW1 Jupyter Notebook

Mon, 20th Jan No class (MLK Day)

Wed, 22nd Jan Lecture 3 : Linear Algebra Review, Least Squares

Mon, 27th Jan Lecture 4 : Linear Regression, part I

HW 1 due

Wed, 29th Jan Lecture 5 : Linear Regression, part II

HW 2 released

Mon, 3rd Feb Lecture 6 : Overfitting, Bias/variance tradeoff, Evaluation

Wed, 5th Feb Lecture 7 : Naive Bayes / Logistic Regression, part I

Mon, 10th Feb Lecture 8 : Naive Bayes / Logistic Regression, part II

HW 3 released

HW 2 due

Wed, 12th Feb Lecture 9 : Multi-class Classification/Perceptron
  • KM, Ch. 8.5

Mon, 17th Feb Lecture 10 : SVM, part I

HW 4 released

HW 3 due

Wed, 19th Feb Lecture 11 : SVM, part II

Mon, 24th Feb Lecture 12 : Nearest Neighbors

HW 4 due

Wed, 26th Feb Mid-term exam

Mon, 2nd Mar Lecture 14 : Decision Trees
  • CIML,
  • KM, Ch. 16.2
  • ESL, Ch. 9.2
  • Ch. 1.3

HW 5 released

Wed, 4th Mar Lecture 15 : Boosting, Random Forest
  • KM, Ch. 16.4

Mon, 9th Mar No Class; Spring Break

Wed, 11th Mar No Class; Spring Break

Mon, 16th Mar Lecture 16 : Neural Networks, Part I
HW 5 due

Wed, 18th Mar Lecture 17 : Neural Networks, Part II

HW 6 released

Mon, 23rd Mar Lecture 18 : Neural Networks, Part III

Wed, 25th Mar Lecture 19 : Clustering, Part I

Mon, 30th Mar Lecture 20 : Clustering, Part II
  • ESL, Ch. 14.3.1-14.3.9

HW 7 released

Wed, 1st Apr Pytorch

HW 6 due

Wed, 6th Apr Lecture 21 : EM

HW 6 due

Wed, 8th Apr Lecture 22 : Dimensionality Reduction

Mon, 13th Apr Lecture 23 : Online Learning

Wed, 15th Apr Lecture 24 : Reinforcement Learning

HW 7 one notebook due

Mon, 20th Apr Guest Lecture | Special Topics

Wed, 22nd Apr Guest Lecture | Special Topics

Mon, 27th Apr Lecture 25 : Last Lecture | Final Exam Review

HW 7 remaining notebooks due

Wed, 29th April Final Exam