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MAT 801: Mathematics for AI
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Module 1: Vectors and Matrix Operations
Introduction to programming
Python basic data structurers
Linear Algebra: Vector Spaces
Essence of Linear Algebra
Solving Real-World Problems with NumPy.
Discussion and Reflection
Reflect on Your Journey
Introduction to programming exercises
Data structurers activities
Module 2: Eigenvalues and Eigenvectors
Eigenvectors and Eigenvalues
Task 1: Review the Theory of Spectral Decomposition
Spectral Decomposition with Visualizations
Task 2: Implement Spectral Decomposition in Python
Principal Component Analysis (PCA): Covariance Matrix
Principal Component Analysis (PCA): Implementation
StatQuest: Linear Discriminant Analysis (LDA)
Join the Conversation
Mini project
Valuing and Take Home Message
Test Your Knowledge
Module 3: Singular Value Decomposition (SVD)
LU Decomposition
Diagonalization
LU Decomposition-2
QR Factorization Module II
Singular Value Decomposition in Python
Solving linear systems using SVD
Understanding the Geometric Interpretation
Valuing and Take Home
Module 4: Review of Basic Calculus Concepts
Pre-assessment
Evaluating a Limit by Factoring
Continuity and Differentiability
Implicit Differentiation for Calculus
Discussion and Reflection
Gradient Descent in Optimization
Module 5: Review of Multivariate Calculus Concepts
Domain of a Multivariable Function
Partial Derivatives
Div and Curl of Vector Fields in Calculus
Introduction to double and triple integrals
Evaluating a Double Integral
Evaluating a Trippe Integral
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Self Reflection
Test Your Knowledge
Module 6: Gradient Descent Algorithm
Pre-Assessment
Introduction to Optimization
Warm Up Quiz
Illustration of Gradient Descent
Solve Equations Using Gradient Descent
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Apply What You've Learned
Module 7: Constrained Optimization and Lagrange Multipliers
Pre-Assessment
Warm Up Quiz
Use of Lagrange Multipliers in Constrained Optimization
Derivation of the extrema of a function using the Lagrange Multiplier technique
Derivation of the extrema of a function using the KKT technique
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Module 8: Probability Theory
Pre-assessment Quiz
Introduction to Probability
Discrete and Continuous Random Variables
Expectation and Variance of a Random Variable
Discussion and Reflection
Apply What You've Learned
Module 9: Probability Distributions
Pre-assessment
Binomial Distribution
Poisson Distribution
Applications of Probability Distributions in AI
Valuing and Take Home
Module 10: Statistical Inference
Introduction to Theory of Estimation
Point Estimation and interval Estimation.
Properties of Good Estimators
Join the Conversation
Reflect on Your Journey
Pre-assessment
Apply What You've Learned
Module 11: Regression Analysis
Introduction to Regression Analysis
FINAL EXAMINATIONS
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Course info
MAT 801: Mathematics for AI
Lecturer:
Group A
Lecturer:
Irene Sitawa
Course Duration in Hours
:
45
Skill Level
:
Beginner
Course rating
:
Course Objectives
:
