skip to page content SBU
Carnegie Mellon University
95-828 Machine Learning for Problem Solving
Spring 2018



Time: Tue & Thu 4:30PM - 5:50PM
Place: HBH A301


Time: Fri 6:00PM - 7:30PM (Jan 26th - Mar 9th)
Place: HBH 1204


Instructor: Leman Akoglu
  • Office hours: Thu 2-3 PM; also, by appointment
  • Mini 4 Time: Fri 3-4PM (starts April 6)
  • Office: HBH 2118C, office ph 412-268-30 four three
  • Email: invert ( @ lakoglu)

Teaching Assistants:
Runshan Fu
  • Office hours: Wed 5-6 PM
  • Location: TA room HBH 3034
  • Email:invert ( @ runshanf)
Abhinav Maurya
  • Office hours: Mon 3-5 PM
  • Location: TA room HBH 3034
  • Email:invert ( @ amaurya)

Darshan Mohan
  • Email:invert ( @ darshanm)
  • Office hours: by appointment
Yashu Pant
  • Email:invert ( @ ypant)
  • Office hours: by appointment


Machine Learning (ML) is centered around automated methods that improve their own performance through learning patterns in data, and then using the uncovered patterns to predict the future and make decisions. ML is heavily used in a wide variety of domains such as business, finance, healthcare, security, etc. for problems including display advertising, fraud detection, disease diagnosis and treatment, face/speech/handwriting/object recognition, automated navigation, to name a few. See this for an extended introduction.

"If I had an hour to solve a problem I'd spend 55 minutes thinking about the problem and 5 minutes thinking about solutions." -- Albert Einstein
"A problem well put is half solved." -- John Dewey

This course aims to equip students with the practical knowledge and experience of recognizing and formulating machine learning problems in the wild, as well as of applying machine learning techniques effectively in practice. The emphasis will be on learning and practicing the machine learning process, involving the cycle of feature design, modeling, and scaling.

"All models are wrong, but some models are useful." -- George Box

As there exists "no free lunch", we will cover a wide range of different models and learning algorithms, which can be applied to a variety of problems and have varying speed-accuracy-scalability-interpretability tradeoffs. In particular, the topics include generalized linear models, decision trees, Bayesian networks, feature selection, ensemble methods, semi-supervised learning, density estimation, latent factor models, network-based classification, and sequence models. See the syllabus for more.

This course is designed to give a graduate-level student a thorough grounding in the methodologies, technologies, and best practices used in machine learning. This course does not assume any prior exposure to machine learning theory or practice. Undergraduates need instructor's permission to enroll. PhD students can either enroll or by permission audit the course.

Learning Objectives

By the end of this class, students will
  • learn the main concepts, methodologies, and tools for machine learning
  • be able to recognize machine learning tasks in real-world problems
  • develop the critical thinking for comparing and contrasting models for a given task
  • learn to reliably perform model selection and evaluation
  • gain the experience of applying the data science process to various problems end-to-end

BULLETIN BOARD and other info

  • For course material, assignments, announcements, and grades please see the Canvas.
  • For questions and discussions please use Piazza.
  • Carnegie Mellon 2017-2018 Official academic calendar


There is no official textbook for the course. I will post all the lecture notes and several readings on course website.

Below you can find a list of recommended reading. We will follow different parts of these various books.
I recommend the top 3 books in this list as regular reading for the course, and the rest for consulting various subjects
and for further reading. Further reading: see the list here and Amazon's best selling ML books.


Fake (ML) protest    

Last updated by Leman Akoglu, Mar 2018