Autoplay
Autocomplete
Previous Lesson
Complete and Continue
Probability and Stats for Computer Science
Intro to Probability and Statistics
Course Moved - Click For Link
Course Intro, Motivation, Methodology (8:51)
Code Env Setup, First Look at Data and Visualization (30:59)
Basics of Stats, Sources and Types of Data, One-Hot Vector Encoding (34:25)
Data Types Continued. Visualizations, Histograms (21:42)
Central Tendency, Averages, Median, Quartiles, Boxplots (29:21)
Dispersion, Variance, Standard Deviation, More Visualizations (45:06)
Probability Basics
Quantifying Chances, Entropy and Information; Motivation (17:26)
Probability, Events, Combining Events, Rationale and Scientific Discovery (50:01)
Simulating Coin Flips, Probability Code, Reproducible Experiments (29:48)
Rules of Probability, Addition, Product, Conditional Probability (31:10)
Application of Conditional Probability, Bayes Rule, Rationale and Importance (28:49)
Spam Detection Maths - Bayes Rule Real World (11:20)
Real World Bayes Rule Case Study - Spam Detection Code (27:33)
Random Variables, Basics and Rationale (19:11)
Joint and Marginal Probabilities (25:41)
Distributions Introduction, Discrete Distributions, PMF (37:45)
Discrete Distributions in Code, Visualization (7:38)
Continuous Distributions, Uniform, Normal, PDF (43:23)
Continuous Distributions Code, Sleep Analysis Case Study (22:20)
Higher Dimentional Joints, Visualization, Contour Plots, Dependence (44:43)
Applied Probability -- Closer to the Real World
Expectation, Expected Values, Rationale and Importance (15:13)
Information, Entropy, Order and Disorder, Application Areas (46:59)
Entropy Application - Machine Learning with Decision Trees (24:56)
Comparing Distributions, Divergence, KL-Divergence (12:27)
What's Wrong with Point Estimates; Bayesian Statistics, MCMC, PyMC3 (27:07)
Teach online with
Comparing Distributions, Divergence, KL-Divergence
Lesson content locked
If you're already enrolled,
you'll need to login
.
Enroll in Course to Unlock