Course curriculum
-
1
Introduction to Course
- Introduction to Instructor and AISciences
- Introduction To Instructor
- Focus of the Course
-
2
Probability vs Statistics
- Probability vs Statistics
-
3
Sets
- Definition of Set
- Definition of Set Exercise 01
- Definition of Set Solution 01
- Definition of Set Exercise 02
- Definition of Set Solution 02
- Cardinality of a Set
- Subsets PowerSet UniversalSet
- Python Practice Subsets
- PowerSets Solution
- Operations
- Operations Exercise 01
- Operations Solution 01
- Operations Exercise 02
- Operations Solution 02
- Operations Exercise 03
- Operations Solution 03
- Python Practice Operations
- VennDiagrams Operations
- Homework
-
4
Experiment
- Random Experiment
- Outcome and Sample Space
- Outcome and Sample Space Exercise 01
- Outcome and Sample Space Solution 01
- Event
- Event Exercise 01
- Event Solution 01
- Event Exercise 02
- Event Solution 02
- Recap and Homework
-
5
Probability Model
- Probability Model
- Probability Axioms
- Probability Axioms Derivations
- Probability Axioms Derivations Exercise 01
- Probability Axioms Derivations Solution 01
- Probablility Models Example
- Probablility Models More Examples
- Probablility Models Continous
- Conditional Probability
- Conditional Probability Example
- Conditional Probability Formula
- Conditional Probability in Machine Learning
- Conditional Probability Total Probability Theorem
- Probablility Models Independence
- Probablility Models Conditional Independence
- Probablility Models Conditional Independence Exercise 01
- Probablility Models Conditional Independence Solution 01
- Probablility Models BayesRule
- Probablility Models towards Random Variables
- HomeWork
-
6
Random Variables
- Introduction
- Random Variables Examples
- Random Variables Examples Exercise 01
- Random Variables Examples Solution 01
- Bernulli Random Variables
- Bernulli Trail Python Practice
- Bernulli Trail Python Practice Exercise 01
- Bernulli Trail Python Practice Solution 01
- Geometric Random Variable
- Geometric Random Variable Normalization Proof Optional
- Geometric Random Variable Python Practice
- Binomial Random Variables
- Binomial Python Practice
- Random Variables in Real DataSets
- Random Variables in Real DataSets Exercise 01
- Random Variables in Real DataSets Solution 01
- Homework
-
7
Continous Random Variables
- Zero Probability to Individual Values
- Zero Probability to Individual Values Exercise 01
- Zero Probability to Individual Values Solution 01
- Probability Density Functions
- Probability Density Functions Exercise 01
- Probability Density Functions Solution 01
- Uniform Distribution
- Uniform Distribution Exercise 01
- Uniform Distribution Solution 01
- Uniform Distribution Python
- Exponential
- Exponential Exercise 01
- Exponential Solution 01
- Exponential Python
- Distributions Introduction using Scipy Module Python
- Continuous Distributions using Scipy Module Python
- Data Preparation for Sleep Analysis
- Sleep Analysis Project
- Gaussian Random Variables
- Gaussian Random Variables Exercise 01
- Gaussian Random Variables Solution 01
- Gaussian Python
- Transformation of Random Variables
- Homework
-
8
Expectations
- Definition
- Sample Mean
- Law of Large Numbers
- Law of Large Numbers Famous Distributions
- Law of Large Numbers Famous Distributions Python
- Variance
- Homework
- Data Introduction
- Preprocessing
- Tokenization and Stemming
- Making a Dictionary
- Basic Stats
- Probability Calculation
-
9
Project Bayes Classifier
- Project Bayes Classifier From Scratch
-
10
Multiple Random Variables
- Joint Distributions
- Joint Distributions Exercise 01
- Joint Distributions Solution 01
- Joint Distributions Exercise 02
- Joint Distributions Solution 02
- Joint Distributions Exercise 03
- Joint Distributions Solution 03
- Multivariate Gaussian
- Conditioning Independence
- Classification
- Naive Bayes Classification
- Regression
- Curse of Dimensionality
- Homework
-
11
Optional Estimation
- Parametric Distributions
- MLE
- LogLiklihood
- MAP
- Logistic Regression
- Ridge Regression
- DNN
-
12
Mathematical Derivations for Math Lovers (Optional)
- Permutations
- Combinations
- Binomial Random Variable
- Logistic Regression Formulation
- Logistic Regression Derivation