CTU-MATH317.AU1
Probability, Statistics, and Uncertainty Modeling
- Practice in 20 Hands-On Labs — nothing to install
- 5 Interactive Lessons and 49 topics mapped to the official exam objectives
Self-paced · 1 year access
20 Hands-On LiveLabs
Practice real IT tasks in guided environments.
- Real environments
- Auto-graded
- No installation
5Interactive Lessons
49Topics
20LiveLab
80Flashcards
80Glossary of terms
01 / Lessons & labs
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Lessons
5 Interactive Lessons · 49 topics01 Modeling Uncertainty with Probability Distributions 8 topics · 3 LiveLab +
- Representing Data
- Summarizing and Visualizing Data
- The Basics of Probability and Probability Distributions
- Hypothesis Testing
- Basic Problems in Machine Learning
- Sample Spaces and Events
- The Counting Approach to Probabilities
- Set-Wise View of Events
3 LiveLab in this lesson — see the labs panel →
02 Probabilistic Reasoning with Bayes’ Theorem 7 topics · 3 LiveLab +
- Conditional Probabilities and Independence
- The Bayes Rule
- The Basics of Probability Distributions
- Distribution Independence and Conditionals
- Summarizing Distributions
- Compound Distributions
- Functions of Random Variables (*)
3 LiveLab in this lesson — see the labs panel →
03 Parameter Estimation with Maximum Likelihood 7 topics · 2 LiveLab +
- Maximum Likelihood Estimation
- Reconstructing Common Distributions from Data
- Mixture of Distributions: The EM Algorithm
- Kernel Density Estimation
- Reducing Reconstruction Variance
- The Bias-Variance Trade-Off
- Popular Distributions Used as Conjugate Priors (*)
2 LiveLab in this lesson — see the labs panel →
04 Statistical Inference and Hypothesis Testing 11 topics · 5 LiveLab +
- The Basics of Regression
- Two Perspectives on Linear Regression
- Solutions to Linear Regression
- Handling Categorical Predictors
- Overfitting and Regularization
- A Probabilistic View of Regularization
- Evaluating Linear Regression
- Nonlinear Regression
- Generative Probabilistic Models
- Loss-Based Formulations: A Probabilistic View
- Beyond Classification: Ordered Logit Model
5 LiveLab in this lesson — see the labs panel →
05 Advanced Applications of Bayes’ Theorem 16 topics · 7 LiveLab +
- The Central Limit Theorem
- Sampling Distribution and Standard Error
- The Basics of Hypothesis Testing
- Hypothesis Tests For Differences in Means
- χ2-Hypothesis Tests
- Analysis of Variance (ANOVA)
- Machine Learning Applications of Hypothesis Testing
- Markov Chains
- Machine Learning Applications of Markov Chains
- Markov Chains to Generative Models
- Hidden Markov Models
- Applications of Hidden Markov Models
- Jensen’s Inequality
- Markov and Chebyshev Inequalities
- Approximations for Sums of Random Variables
- Tail Inequalities Versus Approximation Estimates
7 LiveLab in this lesson — see the labs panel →
Hands-On Labs Our edge
20 LiveLabs- Preparing Data for Regression and Visualization
- Performing Hypothesis Testing
- Modeling Sensor Noise in Robotics
- Implementing the Bayes Classifier
- Training a Naïve Bayes Model
- Creating a Naïve Bayes Spam Classifier
- Implementing the Bias-Variance Trade-Off
- Working with Conjugate Priors and Estimating Parameters
- Training a Linear Regression Model
- Implementing Lasso Regression
- Implementing Non-Linear Transformations of Predictors
- Implementing Multinomial Logistic Regression
- Training a Logistic Regression Model
- Using Sampling to Convert Bimodal Data to a Normal Distribution
- Evaluating AI Model Accuracy with Statistical Tests
- Testing Hypotheses: Type I and II Errors
- Calculating and Interpreting Confidence Intervals
- Using an HMM Model
- Applying Markov and Chebyshev Inequalities
- Applying Chernoff Bounds and Hoeffding Inequalities
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