Coronavirus (COVID-19) Update
Fall classes are offered remotely—either live-online with an instructor, entirely self-paced, or in a blended online format. Please check our coronavirus update page for our latest announcements.
Introduction to Machine Learning and Data Mining
Machine learning and data mining are at the center of a powerful movement driving the tech industry. Companies depend on practitioners of machine learning to create products that parse, reduce, simplify, and categorize data, and then extract actionable intelligence from that data. When you know machine learning, a key technology driving Big Data, you secure a competitive edge in exciting careers in the data sciences.
In this course, you will learn machine learning concepts, terms and methodology. You will gain an intuitive understanding of the mathematics underlying it by understanding how actual applications are built. You will understand how these algorithms drive real-world applications such as search engines, image analysis, biometrics, industrial automation, and market segmentation.
We will establish a basic understanding of supervised learning and Bayesian classifiers using the histogram as a starting point, before exploring the design and application of practical and useful classifiers such as linear machines and decision trees. You will learn concepts in unsupervised learning and clustering algorithms such as expectation maximization and k-means clustering. The course concludes with the application of neural networks in machine learning.
Using examples to guide you through foundational concepts and pseudocode, you will have the opportunity to employ live algorithms to facilitate visual understanding. You are also encouraged to use the pseudocode as a reference to create your own programs. In-class quizzes and group activities and discussion, will help you gauge learning. Homework assignments are designed for in-depth practice.
- Histograms and Bayesian classifiers
- Principal component analysis
- Linear classifiers and regression
- Classifier performance evaluation
- Expectation maximization algorithm
- K-Means algorithm
- Hidden Markov models
- Ensemble learning and Decision trees
- Neural networks
* Skills needed: It is strongly recommended that you have some familiarity with Python programming before enrolling in this course. Python is one of the most popular languages used in AI and machine learning. Learning Python is easy if you already know how to program in another programming language, such as R, C++, Java, Mathematica or Matlab.
If you are new to programming and wish to learn Python by enrolling in a course, we suggest "Python for Machine Learning and Artificial Intelligence, Essentials". For a free, self-study program that meets this course requirement, use the keywords "Microsoft edX Introduction to Python for Data Science" in a search engine.
Sections Open for Enrollment:
|Date:||Start Time:||End Time:||Meeting Type:||Location:|
|Thu, 09-24-2020||6:30 p.m.||9:30 p.m.||Live-Online||ONLINE|
|Thu, 10-01-2020||6:30 p.m.||9:30 p.m.||Live-Online||ONLINE|
|Thu, 10-08-2020||6:30 p.m.||9:30 p.m.||Live-Online||ONLINE|
|Thu, 10-15-2020||6:30 p.m.||9:30 p.m.||Live-Online||ONLINE|
|Thu, 10-22-2020||6:30 p.m.||9:30 p.m.||Live-Online||ONLINE|
|Thu, 10-29-2020||6:30 p.m.||9:30 p.m.||Live-Online||ONLINE|
|Thu, 11-05-2020||6:30 p.m.||9:30 p.m.||Live-Online||ONLINE|
|Thu, 11-12-2020||6:30 p.m.||9:30 p.m.||Live-Online||ONLINE|
|Thu, 11-19-2020||6:30 p.m.||9:30 p.m.||Live-Online||ONLINE|
|Thu, 12-03-2020||6:30 p.m.||9:30 p.m.||Live-Online||ONLINE|
Ask us any questions you may have about this course.