MPCS 53111 Machine Learning (Spring 2018)

Section 1
Instructor(s) Chaudhary, Amitabh (amitabh)
Location Ryerson 251
Meeting Times Friday 5:30pm - 8:30pm
Fulfills Elective Specialization - Data Analytics (DA-1) Specialization - High Performance Computing (HPC-2)

Syllabus

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 neural networks; clustering using k-means, expectation-maximization, hierarchical approaches, and density-based techniques; 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.

Course Prerequisites

1. B+ or above in MPCS 51042 Python Programming (or in Programming core requirement with prior knowledge of Python)
2. B+ or above in MPCS 55001 Algorithms
3. B or above in MPCS 53110 Foundations of Computational Data Analysis (or Data Analysis placement exam)

If you are concurrently taking Algorithms with Machine Learning, a B+ or higher in MPCS 50103 Math for Computer Science

If your grades in the above classes do not meet the minimum requirements set above, please contact the instructor to discuss your background.

Other Prerequisites

Programming in Python in necessary for the class. The following topics are required: use of lists, dictionaries, conditionals, classes, and file i/o.

Students must have attended the Python workshop, have previous familiarity with these topics or be willing to teach themselves. Knowledge of this material will be expected.

Overlapping Classes

This class is scheduled at a time that does not conflict with any other classes this quarter.