Course Overview

This course provides an introduction to machine learning and statistical pattern recognition. We will cover 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 covering a variety of real-world 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 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.

Staff Contact Info


  Gaun-Lin Chao (SV)
  Madhumitha Harishankar (SV)
  Michael Weber (SV)
Haewon Jeong (Pitt)
  Joao Saude (Pitt)
  Rohan Varma (Pitt)

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)

Date Topics Reading HW
8/28 Introduction KM, Ch. 1 Quiz solutions
8/30 Point Estimation: MLE, MAP TM, Estimating Probabilities
KM, Ch. 2 (for a refresh in probability)
9/4 Decision Theory, Linear Algebra Review Math4ML (review/refresher) HW 1 released
9/6 Linear Regression, Part I KM, Ch. 7.1-7.3
Deep Learning Book, Ch. 5*
9/11 Linear Regression, Part II KM, Ch. 7.4-7.6
Intro to regression
9/13 Overfitting / Regularization Deep Learning, Ch. 5.2-5.4
KM, Ch. 6.4
HW 1 due
HW 2 released
9/18 Naive Bayes CIML, Ch. 9
KM, Ch. 3.5
9/20 Logistic Regression KM, Ch. 8.1-8.4, 8.6
Discriminative vs. Generative
9/25 Multiclass Classification KM, Ch. 8.5 HW 2 due
9/27 SVM, Part I ESL, Ch. 12
KM Ch. 28
Kernel Methods
HW 3 released
10/2 SVM, Part II KM Ch. 28
Kernel Methods
10/4 SVM, Part III Generalizability
Advanced Reading - Part 1
Advanced Reading - Part 2
HW 3 due
10/9 In-Class Midterm
10/11 Nearest Neighbors CIML, Ch. 3.1-3.2 HW 4 released
10/16 Decision Trees CIML, Ch. 1.3
KM, Ch. 16.2
ESL, Ch. 9.2
10/18 Boosting KM, Ch. 16.4
10/23 Neural Networks, Part I Learning Deep Architectures for AI
HW 4 due
10/25 Neural Networks, Part II Neural Networks and Deep Learning, Ch.3
Regularization for Deep Learning
HW 5 released
10/30 Neural Networks, Part III RNN
11/1 Clustering, Part I CIML, Ch. 15.1
11/6 Clustering, Part II ESL, Ch. 14.3.1-14.3.9 HW 5 due
11/8 EM KM, Ch. 11.1-11.5 HW 6 released
11/13 Dimensionality Reduction PCA
Independent Component Analysis
11/15 Online Learning Introduction to Online Learning
11/20 Reinforcement Learning
Class canceled
HW 6 due
11/22 No class (Thanksgiving)
11/27 Fairness and Accountability in Machine Learning
Guest Lecture: Anupam Datta
Influence-directed Explanations for Deep Convolutional Networks
11/29 Reinforcement Learning
Recitation: Large-scale Machine Learning
Reinforcement Learning: A Survey
A Brief Introduction to Reinforcement Learning
12/4 ML Research Lightning Talks
12/6 Final Lecture
Practice Exam
12/11 Final Exam