Course Course Summary Section 1 Content Section 1 Content Left Section 1 Content Right Credit Type: Course ACE ID: SKIL-0251 Organization: Internal Revenue Service Location: Online Length: 46 hours Dates Offered: 6/1/2023 - 5/31/2026 Credit Recommendation & Competencies Section 2 Content Section 2 Content Left Section 2 Content Right Level Credits (SH) Subject Upper-Division Baccalaureate 1 Problem Solving with Python Upper-Division Baccalaureate 3 Mathematics for Machine Learning Description Section 3 Content Section 3 Content Left Section 3 Content Right 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 Section 4 Content Section 4 Content Left Section 4 Content Right Instructional Strategies: Computer Based Training Laboratory Practical Exercises Methods of Assessment: Examinations Quizzes Minimum Passing Score: 70% Supplemental Materials Section 5 Content Section 5 Content Left Section 5 Content Right Section 6 Content Section 6 Content Left Section 6 Content Right Button Content Rail Content 1 Other offerings from Internal Revenue Service Apprentice Java Developer to Senior Java Developer (SKIL-0229) AWS Certified Solutions Architect - Associate (SKIL-0252) AWS Certified Solutions Architect - Professional (SKIL-0253) C Programming Proficiency (SKIL-0262) Certified Business Analysis Professional (CBAP) (SKIL-0172) Certified Information Systems Security Professional (CISSP) 2021 Training (SKIL-0241) Cloud Computing for Decision-makers and Leaders (SKIL-0269) CloudOps Apprentice to CloudOps Engineer (SKIL-0237) CompTIA Linux+ (SKIL-0257) CompTIA Network+ (SKIL-0256) View All Courses Page Content