MPCS 53111 Machine Learning (Spring 2024)

Section 1
Instructor(s) Chaudhary, Amitabh (amitabh)
Location Ryerson 276
Meeting Times Wednesday 5:30pm - 8:30pm
Fulfills Elective Specialization - Data Analytics (DA-1) Specialization - High Performance Computing (HPC-2)


This course introduces the fundamental concepts and techniques in data mining, machine learning, and statistical modeling, and the practical know-how to apply them to real-world data through Python-based software. The course examines in detail topics in both supervised and unsupervised learning. These include linear and logistic regression and regularization; classification using decision trees, nearest neighbors, naive Bayes, boosting, random trees, and artificial/convolutional neural networks; clustering using k-means and expectation-maximization; and dimensionality reduction through PCA and SVD. Students use Python and Python libraries such as NumPy, SciPy, matplotlib, and pandas for for implementing algorithms and analyzing data.

Apart from lectures, we conduct optional but strongly recommended problem sessions.  During these the TAs present  homework solutions, and other optional material.  These are the only source for homework solutions; in particular, we do not  publish any solutions.  Recording or streaming the sessions are also not planned.  In Spring, 2019, the problem sessions are most likely to be held on Sunday afternoons; but they may be moved to Saturdays based on TA availability.

Course Prerequisites

B+ or above in MPCS 51042 Python Programming, B+ or better in any other Core Programming class with prior knowledge of Python, or Core Programming waiver.

B or above in MPCS 55001 Algorithms.

MPCS 53110 Foundations of Computational Data Analysis (or passing the Data Analysis placement exam). A C+ or better in MPCS 53110 is required; a B or better in MPCS 53110 is recommended. If you have earned at least a C+ in MPCS 53110, but lower than a B, you should reach out to the instructor to discuss if it is advisable to take the course.

Other Prerequisites

Univariate Calculus and Basic Multivariate Calculus (double integrals, partial derivatives, integration-by-parts, Taylor series).

This course requires competency in Unix and Linux. If you attended the MPCS Unix Bootcamp you covered the required material. If you did not, please review the UChicago CS Student Resource Guide here:

This course assumes both mathematical maturity and programming fluency. In particular, students are expected to code complicated machine learning algorithms from scratch (without a template) and debug them on their own.

Non-MPCS students must complete the above prerequisites to be eligible to take this class.

Overlapping Classes

This class is scheduled at a time that conflicts with these other classes:

  • MPCS 51215-1 -- Topics in Software: Making an Impact
  • MPCS 53020-1 -- Foundations of Database Systems
  • MPCS 51400-1 -- Functional Programming
  • MPCS 52060-1 -- Parallel Programming
  • MPCS 54001-1 -- Networks

Eligible Programs

MS in Molecular Engineering MA in Computational Social Science (Year 2) Bx/MS in Computer Science (Option 1: Research-Oriented) Bx/MS in Computer Science (Option 2: Professionally-oriented - CS Majors) Bx/MS in Computer Science (Option 3: Profesionally-oriented - Non-CS Majors) Masters Program in Computer Science