Unsupervised Machine Learning with Python

Course Outcome:

After taking this course, students will be able to understand and implement in Python algorithms of Unsupervised Machine Learning and apply them to real-world datasets.

Course Topics and Approach:

Unsupervised Machine Learning involves finding patterns in datasets. The core of this course involves study of the following algorithms:

Clustering: Hierarchical, DBSCAN, K Means & Gaussian Mixture Model

Dimension Reduction: Principal Component Analysis

Unlike many other courses, this course:

• Has a detailed presentation of the the math underlying the above algorithms, including normal distributions, expectation maximization, and singular value decomposition.

• Has a detailed explanation of how algorithms are converted into Python code with lectures on code design and use of vectorization

• Has questions (programming and theory) and solutions that allow learners to get practice with the course material

The course codes are then used to address case studies involving real-world data to perform dimension reduction/clustering for the Iris Flowers Dataset, MNIST Digits Dataset (images), and BBC Text Dataset (articles).

Course Audience:

This course is designed for:

• Scientists, engineers, and programmers and others interested in machine learning/data science

• No prior experience with machine learning is needed

• Students should have knowledge of

  • Basic linear algebra (vectors, transpose, matrices, matrix multiplication, inverses, determinants, linear spaces)

  • Basic probability and statistics (mean, covariance matrices, normal distributions)

  • Python 3 programming

Students should have a Python installation, such as the Anaconda platform, on their machine with the ability to run programs in the command window and in Jupyter Notebooks

Teaching Style and Resources:

• Course includes many examples with plots and animations used to help students get a better understanding of the material

• Course has many exercises with solutions (theoretical, Jupyter Notebook, and programming) to allow students to gain additional practice

• All resources (presentations, supplementary documents, demos, codes, solutions to exercises) are downloadable from the course Github site.

2021.08.28 Update: 

• Section 9.5: added Autoencoder example

• Section 9.6: added this new section with an Autoencoder Demo

2021.11.02 Update:

• Sections 2.3, 2.4, 3.4, 4.3: updates so codes can run in more recent versions of python and matplotlib and updates to presentations to point out the changes