Machine Learning Foundation (Math Emphasis) | Exploring Statistics, Algorithms and Neural Networks (TTML5504)

Course Objectives

This “skills-centric” course is about 50% hands-on lab and 50% lecture, with extensive practical exercises designed to reinforce fundamental skills, concepts and best practices taught throughout the course.  Throughout the course students will learn about and explore popular machine learning algorithms, their applicability and limitations and practical application of these methods in a machine learning environment. This course reviews key foundational mathematics and introduces students to the algorithms of Data Science. 

Working in a hands-on learning environment, students will explore:

• Popular machine learning algorithms, their applicability and limitations
• Practical application of these methods in a machine learning environment
• Practical use cases and limitations of algorithms
• Core machine learning mathematics and statistics
• Supervised Learning vs. Unsupervised Learning
• Classification Algorithms including Support Vector Machines, Discriminant Analysis, Naïve Bayes, and Nearest Neighbor
• Regression Algorithms including Linear and Logistic Regression, Generalized Linear Modeling, Support Vector Regression, Decision Trees, k-Nearest Neighbors (KNN)
• Clustering Algorithms including k-Means, Fuzzy clustering, Gaussian Mixture
• Neural Networks including Hidden Markov (HMM), Recurrent (RNN) and Long-Short Term Memory (LSTM)
• Dimensionality Reduction, Single Value Decomposition (SVD), Principle Component Analysis (PCA)
• How to choose an algorithm for a given problem
• How to choose parameters and activation functions
• Ensemble methods

Need different skills or topics?  If your team requires different topics or tools, additional skills or custom approach, this course may be further adjusted to accommodate.  We offer additional AI, machine learning, data science, programming, Python/R and other related topics that may be blended with this course for a track that best suits your needs. Our team will collaborate with you to understand your needs and will target the course to focus on your specific learning objectives and goals.

Course Prerequisites

Although this course is highly technical in nature, it is a foundation-level machine learning class for Intermediate skilled team members who are relatively new to AI and machine learning. This course as-is is not for advanced participants.

This course is geared for Data Analysts, Programmers, Administrators, Architects, and Managers interested in a deeper exploration of common algorithms and best practices in machine learning.  Attending students should have

• Strong foundational mathematics skills in Linear Algebra and Probability, to start learning about and using basic machine learning algorithms and concepts
• Basic Python Skills.  Attendees without Python background may view labs as follow along exercises or team with others to complete them. (NOTE: This course is also offered in R or Scala – please inquire for details)
• Basic Linux skills, including familiarity with command-line options such as ls, cd, cp, and su

Course Agenda

Please note that this list of topics is based on our standard course offering, evolved from typical industry uses and trends. We’ll work with you to tune this course and level of coverage to target the skills you need most.

1. Core Machine Learning Mathematics Review

• Statistics Overview and Review
• Mean, Median, Variance, and deviation
• Normal / Gaussian Distribution

2. Probability Review

• Probability Theory
• Discrete Probability Distributions
• Continuous Probability Distributions
• Measure-Theoretic Probability Theory
• Central Limit and Normal Distribution
• Probability Density Function
• Probability in Machine Learning

3. Supervised Learning

• Supervised Learning Explained
• Classification vs. Regression
• Examples of Supervised Learning
• Key supervised algorithms

4. Unsupervised Learning

• Unsupervised Learning
• Clustering
• Examples of Unsupervised Learning
• Key unsupervised algorithms (overview)

5. Regression Algorithms

• Linear Regression
• Logistic Regression
• Support Vector Regression
• Decision Trees
• Random Forests

6. Classification Algorithms

• Bayes Theorem and the Naïve Bayes classifier
• Support Vector Machines
• Discriminant Analysis
• k-Nearest Neighbor (KNN)

7. Clustering Algorithms

• k-Means Clustering
• Fuzzy Clustering
• Gaussian Mixture Models

8. Neural Networks

• Neural Network Basics
• Hidden Markov Models (HMM)
• Recurrent Neural Networks (RNN)
• Long-Short Term Memory Networks (LSTM)

9. Choosing Algorithms

• Choosing between Supervised and Unsupervised algorithms
• Choosing between Classification Algorithms
• Choosing between Regressions
• Choosing Neural Networks
• Choosing Activation Functions

10. Ensemble Methods

• Ensemble Theory and Methods
• Ensemble Classifiers
• Bucket of Models
• Boosting
• Stacking

11. Optional: Topics Survey

• Machine Learning in Python: NumPy, Pandas, SciKit-ML, and MatPlotLIb; NLTK, Keras
• Machine Learning in R
• Machine Learning in Java
• Machine Learning with Apache Madlib
• Hadoop, MapReduce, and Mahout
• Spark and MLLib
• TensorFlow

Course Materials

Student Materials: Each student will receive a Student Guide with course notes, code samples, setp-by-step written lab instructions, software tutorials, diagrams and related reference materials and links (as applicable). Students will also receive related (as applicable) project files, code files, data sets and solutions required for any hands-on work.

Lab Setup Made Simple.   All course labs and solutions, data sets, software, detailed courseware, lab guides and resources (as applicable) are provided for attendees in our easy access, no installation required, remote lab environment. Our tech team will help set up, test and verify lab access for each attendee prior to the course start date, ensuring a smooth start to class and successful hands-on course experience for all participants.