Course

Course Summary
Credit Type:
Course
ACE ID:
SKIL-0251
Organization:
Internal Revenue Service
Location:
Online
Length:
46 hours
Dates Offered:
Credit Recommendation & Competencies
Level Credits (SH) Subject
Upper-Division Baccalaureate 1 Problem Solving with Python
Upper-Division Baccalaureate 3 Mathematics for Machine Learning
Description

Objective:

The course objective is to explore important concepts of mathematics that form the foundation for Machine Learning algorithms, Data Science, and Artificial Intelligence, including probability, statistics, calculus, and linear algebra.

Learning Outcomes:

  • Implement matrix decomposition
  • Compute statistics on and generate samples from data
  • Apply linear and logistic regression, decision trees, distance-based models, Support Vector Machines (SVM), and neural networks
  • Solve optimization problems using linear and integer programming
  • Simulate probabilistic experiments and build Bayesian models to calculate conditional probability
  • Generate and work with probability distributions
  • Explore principal component analysis, recommendation systems, and gradient descent
  • Work with derivatives, linear and quadratic functions, and partial derivatives
  • Perform fundamental operations on matrices
  • Perform statistical and hypothesis tests

General Topics:

  • Discrete Math Concepts and Implementations
  • A Theoretical and Practical Guide to Calculus
  • An Exploration of Linear Algebra
  • Understanding and Implementing Matrix Decomposition
  • Introduction to Statistical Concepts
  • Probability Theory
  • Probability Distributions
  • A Deep Dive into Statistical and Hypothesis Tests
  • The Math Behind Linear and Logistic Regression
  • The Math Behind Decision Trees
  • The Math Behind Distance-based Models
  • Math Behind Support Vector Machines
  • The Math Behind Neural Networks
  • A Deep Dive into Principal Component Analysis
  • A Detailed Look at Recommendation Systems
  • An Exploration of Gradient Descent
Instruction & Assessment

Instructional Strategies:

  • Computer Based Training
  • Laboratory
  • Practical Exercises

Methods of Assessment:

  • Examinations
  • Quizzes

Minimum Passing Score:

70%
Supplemental Materials