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bookOutline.hjson
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{
"intro": {
"title":null,
"sections": {
# "intro":"Introduction",
# "free_book":"A Free Online Textbook",
"notation": "Notation Reference",
"all_distributions":"Random Variable Reference",
"calculators": "Calculators",
# "prob_code": "Probability in Code"
}
},
"part1": {
"title":"Part 1: Core Probability",
"sections": {
"counting":"Counting",
"combinatorics":"Combinatorics",
"probability":"Definition of Probability",
"equally_likely":"Equally Likely Outcomes",
"prob_or":"Probability of <b>or</b>",
"cond_prob":"Conditional Probability",
"independence": "Independence",
"prob_and":"Probability of <b>and</b>",
"law_total":"Law of Total Probability",
"bayes_theorem":"Bayes' Theorem",
# "random_computers":"Randomness in Computers",
"log_probabilities":"Log Probabilities",
"many_flips":"Many Coin Flips"
},
"examples": {
"enigma":"Enigma Machine",
"serendipity":"Serendipity",
"bacteria_evolution":"Bacteria Evolution"
}
},
"part2":{
"title":"Part 2: Random Variables",
"sections": {
"rvs":"Random Variables",
"pmf":"Probability Mass Functions",
"expectation":"Expectation",
"variance":"Variance",
"bernoulli":"Bernoulli Distribution",
"binomial":"Binomial Distribution",
"poisson":"Poisson Distribution",
#"geometric":"Geometric",
#"hyper_geo":"Hyper Geometric",
"continuous":"Continuous Distribution",
"uniform":"Uniform Distribution",
"exponential":"Exponential Distribution",
"normal":"Normal Distribution",
"binomial_approx":"Binomial Approximation"
},
"examples": {
"100_binomial_problems":"100 Binomial Problems",
"winning_series":"Winning Series",
"jury":"Jury Selection",
"grading_eye_inflammation":"Grading Eye Inflamation"
"norm_cdf_calculator":"Gaussian CDF Calculator",
"grades_not_normal":"Grades are Not Normal",
"curse_of_dimensionality":"Curse of Dimensionality"
"prob_baby_delivery":"Probability of Baby Delivery",
# "algorithmic_art":"Algorithmic Art"
}
},
"part3":{
"title":"Part 3: Probabilistic Models",
"sections": {
"joint":"Joint Probability",
"multinomial":"Multinomial",
"continuous_joint":"Continuous Joint",
"inference":"Inference",
"bayesian_networks":"Bayesian Networks",
"independent_vars":"Independence in Variables",
"correlation":"Correlation",
"computational_inference":"General Inference",
},
"examples": {
"dart_logo":"CS109 Logo",
"fairness":"Fairness in AI",
"federalist":"Federalist Paper Authorship",
"name2age":"Name to Age",
"bridge_distribution":"Bridge Distribution",
"tracking_in_2D":"Tracking in 2D"
}
},
"part4":{
"title":"Part 4: Uncertainty Theory",
"sections": {
"beta":"Beta Distribution",
"summation_vars":"Adding Random Variables",
"clt":"Central Limit Theorem",
"samples":"Sampling",
"bootstrapping":"Bootstrapping",
#"parameters":"Uncertainty in Parameters",
"algorithmic_analysis":"Algorithmic Analysis",
#"bounds":"Probability Bounds"
},
"examples": {
"thompson":"Thompson Sampling"
}
},
"part5":{
"title":"Part 5: Machine Learning",
"sections": {
"parameter_estimation":"Parameter Estimation",
"mle":"Maximum Likelihood Estimation",
"map":"Maximum A Posteriori",
"machine_learning":"Machine Learning",
"naive_bayes":"Naïve Bayes",
"log_regression":"Logistic Regression"
},
"examples": {
"mle_demo":"MLE Normal Demo",
"mixture_models":"MLE Mixture Model",
}
},
# "examples": {
# "title":"Examples",
# "sections": {
# "prob_baby_delivery":"Probability of Baby Delivery",
# "fast_dot_com":"Fast.com",
# "greenhouse_effect":"Greenhouse Effect",
# "bridge_distribution":"Bridge Distribution",
# "curse_of_dimensionality":"Curse of Dimensionality",
# "period_tracker":"Period Tracker",
# "fairness":"Fairness",
# "climate_sensitivity":"Climate Sensitivity",
# "age_given_names":"Age Given Name",
# "grades_not_normal":"Grades are Not Normal"
# }
# }
# TODO: Mehran to add as he sees fit
# "part6":{
# "title":"Part 6: Intro to Information Theory",
# "sections": {
# "entropy":"Maximum Likelihood Estimation",
# "cross_entopy":"Cross Entropy",
# "kl_divergence":"KL Divergence",
# }
# }
# COOL EXTRA Examples Ranked Choice Voting
}