Machine Learning

Title Machine Learning (53111)
Quarter Spring 2020
Instructor Amitabh Chaudhary (


This course is open to MPCS students only.

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  These are the only source for homework solutions; in particular, we do not  publish any solutions.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.

Prerequisites (Courses)

1. B+ or above in MPCS 51042 Python Programming (or in core programming requirement with prior knowledge of Python)
2. B+ or above in MPCS 50103 Math for Computer Science or passing the Discrete Math placement exam
3. B+ or above in MPCS 55001 Algorithms
4. B or above in MPCS 53110 Foundations of Computational Data Analysis (or passing the Data Analysis placement exam)

Prerequisites (Other)

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

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.

Not approved for CAPP or MACS students.


Data Analytics Specialization (


Wednesday 5:30-8:30PM


Ryerson 251