Project Autodidact
Project Details: https://insightsbyse.com/projectautodidact/
Scott Ernst Bio: https://insightsbyse.com/aboutscotternst/
Project Contact: InsightsBySE@protonmail.com
Progress Report Scope (S01-M02-D04-AllParts)
Stage 1 of 4: Review of Mathematics, Probability, and Statistics
Module 2 of 3: Statistics, Probability, and Advanced Algebra
Day 4 of 5: Sequences, Series, and Combinatorics
Parts 1 through 9: See below (NOTE: Only 9 parts for these topics)
Summary Of Goals Achieved
- Reviewed similarities and differences between an arithmetic sequence and arithmetic series
- Reviewed definition, concepts, notation, terminology, components, and properties of an arithmetic sequence
- Reviewed definition, concepts, notation, terminology, components, and properties of an arithmetic series
- Reviewed similarities and differences in the applicability of the methods for identifying terms in an arithmetic sequence (difference method and graphical method)
- Reviewed similarities and differences in the applicability of the methods for computing terms in an arithmetic sequence (explicit formula, recursive method, and graphical method)
- Reviewed similarities and differences in the applicability of the methods for computing the sum of an arithmetic series (standard sum formula, alternative sum formula, and iterative addition method)
- Reviewed procedures of the methods for identifying terms in an arithmetic sequence (difference method and graphical method)
- Reviewed procedures of the methods for computing terms in an arithmetic sequence (explicit formula, recursive method, and graphical method)
- Reviewed procedures of the methods for computing the sum of an arithmetic series (standard sum formula (derived from Gauss’s method), alternative sum formula, and iterative addition method)
- Reviewed how computers are utilized for performing the tasks and calculations for (1) identifying terms in an arithmetic sequence, (2) computing terms in an arithmetic sequence, and (3) computing the sum of an arithmetic series
- Reviewed similarities and differences between a geometric sequence and geometric series
- Reviewed definition, concepts, notation, terminology, components, and properties of a geometric sequence
- Reviewed definition, concepts, notation, terminology, components, and properties of a geometric series
- Reviewed similarities and differences in the applicability of the methods for identifying terms in a geometric sequence (explicit formula, recursive identification, ratio-based detection, interpolation, and inverse methods)
- Reviewed similarities and differences in the applicability of the methods for computing terms in a geometric sequence (direct formula application, iterative recursion, ratio derivation, interpolation, and logarithmic solving)
- Reviewed similarities and differences in the applicability of the methods for computing the sum of a geometric series (closed-form finite sum formula, infinite series formula, telescoping derivation, partial summation, and numerical approximation)
- Reviewed procedures of methods for identifying terms in a geometric sequence (explicit formula, recursive identification, ratio-based detection, interpolation, and inverse methods)
- Reviewed procedures of the methods for computing terms in a geometric sequence (direct formula application, iterative recursion, ratio derivation, interpolation, and logarithmic solving)
- Reviewed procedures of the methods for computing the sum of a geometric series (closed-form finite sum formula, infinite series formula, telescoping derivation, partial summation, and numerical approximation)
- Reviewed how computers are utilized for performing the tasks and calculations for (1) identifying terms in a geometric sequence, (2) computing terms in a geometric sequence, and (3) computing the sum of a geometric series
- Reviewed similarities and differences between a permutation without repetition, permutation with repetition, combination without repetition, and combination with repetition
- Reviewed definition, concepts, notation, terminology, components, and properties of a permutation without repetition and permutation with repetition
- Reviewed similarities and differences in the applicability of the methods for solving a problem using a permutation without repetition (direct factorial computation (analytical method), recursive algorithms, dynamic programming, and approximation techniques (e.g., Stirling’s formula))
- Reviewed similarities and differences in the applicability of the methods for solving a problem using a permutation with repetition (direct exponentiation (analytical method), recursive algorithms, dynamic programming, and approximation techniques (e.g., logarithmic scaling))
- Reviewed how computers are utilized for performing the tasks and calculations for solving a problem using a permutation without repetition and permutation with repetition
- Reviewed definition, concepts, notation, terminology, components, and properties of a combination without repetition and combination with repetition
- Reviewed similarities and differences in the applicability of the methods for solving a problem using a combination without repetition (direct factorial-based computation, recursive methods, iterative algorithms (lexicographic ordering), bit-manipulation methods, graph-based approaches, backtracking, greedy heuristics, dynamic programming, Monte Carlo simulation, and binary search)
- Reviewed similarities and differences in the applicability of the methods for solving a problem using a combination with repetition (direct factorial-based computation (by stars-and-bars), recursive methods, iterative algorithms (reverse lexicographic ordering), bit-manipulation methods (with counters), graph-based approaches, backtracking, greedy heuristics, dynamic programming, Monte Carlo simulation, and binary search)
- Reviewed how computers are utilized for performing the tasks and calculations for solving a problem using a combination without repetition and combination with repetition
- Reviewed definition, concepts, notation, terminology, components, and properties of combinatorics
- Reviewed similarities and differences in the applicability of the methods for solving a probability problem using combinatorics
- Reviewed how computers are utilized for performing the tasks and calculations for solving a probability problem using combinatorics
- Reviewed definition, concepts, notation, terminology, components, and properties of types of time series (univariate time series, multivariate time series, stationary time series, non-stationary time series, trend time series, seasonal time series, cyclic time series, continuous time series, discrete time series, additive time series, multiplicative time series, regular time series, and irregular (i.e. random) time series)
- Reviewed procedures for determining the categorical attributes of a time series (number of variables, stationarity, temporal patterns, data composition, time index, and measurement scale)
- Reviewed how computers are utilized for determining the categorical attributes of a time series (number of variables, stationarity, temporal patterns, data composition, time index, and measurement scale)
- Reviewed similarities and differences in the applicability of the methods for modeling and analyzing a time series based on number of variables (univariate time series and multivariate time series)
- Reviewed similarities and differences in the applicability of the methods for modeling and analyzing a time series based on stationarity (stationary time series and non-stationary time series)
- Reviewed similarities and differences in the applicability of the methods for modeling and analyzing a time series based on temporal patterns (trend time series, seasonal time series, and cyclic time series)
- Reviewed similarities and differences in the applicability of the methods for modeling and analyzing a time series based on data composition (additive time series and multiplicative time series)
- Reviewed similarities and differences in the applicability of the methods for modeling and analyzing a time series based on time index (regular time series and irregular (i.e., random) time series)
- Reviewed similarities and differences in the applicability of the methods for modeling and analyzing a time series based on measurement scale (continuous time series and discrete time series)
- Reviewed similarities and differences in the applicability of the methods for modeling and analyzing a time series with multiple categorical attributes (number of variables, stationarity, temporal patterns, data composition, time index, and/ or measurement scale)
- Reviewed similarities and differences in the applicability of the statistical methods for modeling and analyzing types of time series (moving averages, autoregressive (AR) models, moving average (MA) models, autoregressive integrated moving average (ARIMA), seasonal autoregressive integrated moving average (SARIMA), exponential smoothing, state-space models, and decomposition)
- Reviewed similarities and differences in the applicability of the machine learning methods for modeling and analyzing types of time series (random forests, gradient boosting, and support vector machines (SVMs))
- Reviewed similarities and differences in the applicability of the deep learning methods for modeling and analyzing types of time series (recurrent neural networks (RNNs), convolutional neural networks (CNNs), transformers, sequence-to-sequence (Seq2Seq) models, temporal convolutional networks (TCNs), autoencoders, and neural prophet)
- Reviewed similarities and differences in the applicability of the frequency domain methods for modeling and analyzing types of time series (Fourier transform, wavelet transform, and spectral analysis)
- Reviewed similarities and differences in the applicability of the probabilistic and Bayesian methods for modeling and analyzing types of time series (Hidden Markov Models (HMMs), Gaussian Processes, and Bayesian Structural Time Series (BSTS))
- Reviewed similarities and differences in the applicability of the anomaly detection methods for modeling and analyzing types of time series (threshold-based methods, z-score analysis, clustering-based methods, and isolation forests)
- Reviewed similarities and differences in the applicability of the feature engineering and preprocessing methods for modeling and analyzing types of time series (lagged features, differencing, detrending and deseasonalizing, normalization/standardization, and rolling statistics)
- Reviewed similarities and differences in the applicability of the hybrid and ensemble methods for modeling and analyzing types of time series
- Reviewed similarities and differences in the applicability of the visualization and exploratory methods for modeling and analyzing types of time series (time plots, autocorrelation function (ACF), partial autocorrelation function (PACF), seasonal plots, and box plots)
- Reviewed definition, concepts, notation, terminology, components, and properties of combinatorics
- Reviewed definition, concepts, notation, terminology, components, and properties of probability sampling
- Reviewed definition, concepts, notation, terminology, components, and properties of non-probability sampling
- Reviewed similarities and differences in the applicability of the types of probability and non-probability sampling
- Reviewed procedures for using combinatorics for sampling techniques
- Reviewed how computers are utilized for performing the tasks and calculations for using combinatorics for sampling techniques
- Reviewed definition, concepts, notation, terminology, components, and properties of linear recursive sequences
- Reviewed definition, concepts, notation, terminology, components, and properties of non-linear recursive sequences
- Reviewed procedures for methods for analyzing linear recursive sequences (iterative computation, explicit formula derivation, characteristic equation method, generating functions, dynamic programming, and memoization)
- Reviewed procedures for methods for analyzing non-linear recursive sequences (iterative computation, generating functions, dynamic programming, and memoization)
- Reviewed how computers are utilized for performing the tasks and calculations for analyzing linear and non-linear recursive sequences
Part 1 of 9
Goal 1 Statement: Review similarities and differences between an arithmetic sequence and arithmetic series
Goal 1 Plan: Read source materials
Goal 1 Work Product: None
Goal 1 Result: Completed
Goal 2 Statement: Review definition, concepts, notation, terminology, components, and properties of an arithmetic sequence
Goal 2 Plan: Read source materials
Goal 2 Work Product: None
Goal 2 Result: Completed
Goal 3 Statement: Review definition, concepts, notation, terminology, components, and properties of an arithmetic series
Goal 3 Plan: Read source materials
Goal 3 Work Product: None
Goal 3 Result: Completed
Goal 4 Statement: Review similarities and differences in the applicability of the methods for identifying terms in an arithmetic sequence (difference method and graphical method)
Goal 4 Plan: Read source materials
Goal 4 Work Product: None
Goal 4 Result: Completed
Goal 5 Statement: Review similarities and differences in the applicability of the methods for computing terms in an arithmetic sequence (explicit formula, recursive method, and graphical method)
Goal 5 Plan: Read source materials
Goal 5 Work Product: None
Goal 5 Result: Completed
Goal 6 Statement: Review similarities and differences in the applicability of the methods for computing the sum of an arithmetic series (standard sum formula, alternative sum formula, and iterative addition method)
Goal 6 Plan: Read source materials
Goal 6 Work Product: None
Goal 6 Result: Completed
Goal 7 Statement: Review procedures of the methods for identifying terms in an arithmetic sequence (difference method and graphical method)
Goal 7 Plan: Read source materials
Goal 7 Work Product: None
Goal 7 Result: Completed
Goal 8 Statement: Review procedures of the methods for computing terms in an arithmetic sequence (explicit formula, recursive method, and graphical method)
Goal 8 Plan: Read source materials
Goal 8 Work Product: None
Goal 8 Result: Completed
Goal 9 Statement: Review procedures of the methods for computing the sum of an arithmetic series (standard sum formula (derived from Gauss’s method), alternative sum formula, and iterative addition method)
Goal 9 Plan: Read source materials
Goal 9 Work Product: None
Goal 9 Result: Completed
Goal 10 Statement: Review how computers are utilized for performing the tasks and calculations for (1) identifying terms in an arithmetic sequence, (2) computing terms in an arithmetic sequence, and (3) computing the sum of an arithmetic series
Goal 10 Plan: Read source materials
Goal 10 Work Product: None
Goal 10 Result: Completed
Part 2 of 9
Goal 1 Statement: Review similarities and differences between a geometric sequence and geometric series
Goal 1 Plan: Read source materials
Goal 1 Work Product: None
Goal 1 Result: Completed
Goal 2 Statement: Review definition, concepts, notation, terminology, components, and properties of a geometric sequence
Goal 2 Plan: Read source materials
Goal 2 Work Product: None
Goal 2 Result: Completed
Goal 3 Statement: Review definition, concepts, notation, terminology, components, and properties of a geometric series
Goal 3 Plan: Read source materials
Goal 3 Work Product: None
Goal 3 Result: Completed
Goal 4 Statement: Review similarities and differences in the applicability of the methods for identifying terms in a geometric sequence (explicit formula, recursive identification, ratio-based detection, interpolation, and inverse methods)
Goal 4 Plan: Read source materials
Goal 4 Work Product: None
Goal 4 Result: Completed
Goal 5 Statement: Review similarities and differences in the applicability of the methods for computing terms in a geometric sequence (direct formula application, iterative recursion, ratio derivation, interpolation, and logarithmic solving)
Goal 5 Plan: Read source materials
Goal 5 Work Product: None
Goal 5 Result: Completed
Goal 6 Statement: Review similarities and differences in the applicability of the methods for computing the sum of a geometric series (closed-form finite sum formula, infinite series formula, telescoping derivation, partial summation, and numerical approximation)
Goal 6 Plan: Read source materials
Goal 6 Work Product: None
Goal 6 Result: Completed
Goal 7 Statement: Review procedures of methods for identifying terms in a geometric sequence (explicit formula, recursive identification, ratio-based detection, interpolation, and inverse methods)
Goal 7 Plan: Read source materials
Goal 7 Work Product: None
Goal 7 Result: Completed
Goal 8 Statement: Review procedures of the methods for computing terms in a geometric sequence (direct formula application, iterative recursion, ratio derivation, interpolation, and logarithmic solving)
Goal 8 Plan: Read source materials
Goal 8 Work Product: None
Goal 8 Result: Completed
Goal 9 Statement: Review procedures of the methods for computing the sum of a geometric series (closed-form finite sum formula, infinite series formula, telescoping derivation, partial summation, and numerical approximation)
Goal 9 Plan: Read source materials
Goal 9 Work Product: None
Goal 9 Result: Completed
Goal 10 Statement: Review how computers are utilized for performing the tasks and calculations for (1) identifying terms in a geometric sequence, (2) computing terms in a geometric sequence, and (3) computing the sum of a geometric series
Goal 10 Plan: Read source materials
Goal 10 Work Product: None
Goal 10 Result: Completed
Part 3 of 9
Goal 1 Statement: Review similarities and differences between a permutation without repetition, permutation with repetition, combination without repetition, and combination with repetition
Goal 1 Plan: Read source materials
Goal 1 Work Product: None
Goal 1 Result: Completed
Goal 2 Statement: Review definition, concepts, notation, terminology, components, and properties of a permutation without repetition and permutation with repetition
Goal 2 Plan: Read source materials
Goal 2 Work Product: None
Goal 2 Result: Completed
Goal 3 Statement: Review similarities and differences in the applicability of the methods for solving a problem using a permutation without repetition (direct factorial computation (analytical method), recursive algorithms, dynamic programming, and approximation techniques (e.g., Stirling’s formula))
Goal 3 Plan: Read source materials
Goal 3 Work Product: None
Goal 3 Result: Completed
Goal 4 Statement: Review similarities and differences in the applicability of the methods for solving a problem using a permutation with repetition (direct exponentiation (analytical method), recursive algorithms, dynamic programming, and approximation techniques (e.g., logarithmic scaling))
Goal 4 Plan: Read source materials
Goal 4 Work Product: None
Goal 4 Result: Completed
Goal 5 Statement: Review how computers are utilized for performing the tasks and calculations for solving a problem using a permutation without repetition and permutation with repetition
Goal 5 Plan: Read source materials
Goal 5 Work Product: None
Goal 5 Result: Completed
Part 4 of 9
Goal 1 Statement: Review definition, concepts, notation, terminology, components, and properties of a combination without repetition and combination with repetition
Goal 1 Plan: Read source materials
Goal 1 Work Product: None
Goal 1 Result: Completed
Goal 2 Statement: Review similarities and differences in the applicability of the methods for solving a problem using a combination without repetition (direct factorial-based computation, recursive methods, iterative algorithms (lexicographic ordering), bit-manipulation methods, graph-based approaches, backtracking, greedy heuristics, dynamic programming, Monte Carlo simulation, and binary search)
Goal 2 Plan: Read source materials
Goal 2 Work Product: None
Goal 2 Result: Completed
Goal 3 Statement: Review similarities and differences in the applicability of the methods for solving a problem using a combination with repetition (direct factorial-based computation (by stars-and-bars), recursive methods, iterative algorithms (reverse lexicographic ordering), bit-manipulation methods (with counters), graph-based approaches, backtracking, greedy heuristics, dynamic programming, Monte Carlo simulation, and binary search)
Goal 3 Plan: Read source materials
Goal 3 Work Product: None
Goal 3 Result: Completed
Goal 4 Statement: Review how computers are utilized for performing the tasks and calculations for solving a problem using a combination without repetition and combination with repetition
Goal 4 Plan: Read source materials
Goal 4 Work Product: None
Goal 4 Result: Completed
Part 5 of 9
Goal 1 Statement: Review definition, concepts, notation, terminology, components, and properties of combinatorics
Goal 1 Plan: Read source materials
Goal 1 Work Product: None
Goal 1 Result: Completed
Goal 2 Statement: Review similarities and differences in the applicability of the methods for solving a probability problem using combinatorics
Goal 2 Plan: Read source materials
Goal 2 Work Product: None
Goal 2 Result: Completed
Goal 3 Statement: Review how computers are utilized for performing the tasks and calculations for solving a probability problem using combinatorics
Goal 3 Plan: Read source materials
Goal 3 Work Product: None
Goal 3 Result: Completed
Part 6 of 9
Goal 1 Statement: Review definition, concepts, notation, terminology, components, and properties of types of time series (univariate time series, multivariate time series, stationary time series, non-stationary time series, trend time series, seasonal time series, cyclic time series, continuous time series, discrete time series, additive time series, multiplicative time series, regular time series, and irregular (i.e. random) time series)
Goal 1 Plan: Read source materials
Goal 1 Work Product: None
Goal 1 Result: Completed
Goal 2 Statement: Review procedures for determining the categorical attributes of a time series (number of variables, stationarity, temporal patterns, data composition, time index, and measurement scale)
Goal 2 Plan: Read source materials
Goal 2 Work Product: None
Goal 2 Result: Completed
Goal 3 Statement: Review how computers are utilized for determining the categorical attributes of a time series (number of variables, stationarity, temporal patterns, data composition, time index, and measurement scale)
Goal 3 Plan: Read source materials
Goal 3 Work Product: None
Goal 3 Result: Completed
Goal 4 Statement: Review similarities and differences in the applicability of the methods for modeling and analyzing a time series based on number of variables (univariate time series and multivariate time series)
Goal 4 Plan: Read source materials
Goal 4 Work Product: None
Goal 4 Result: Completed
Goal 5 Statement: Review similarities and differences in the applicability of the methods for modeling and analyzing a time series based on stationarity (stationary time series and non-stationary time series)
Goal 5 Plan: Read source materials
Goal 5 Work Product: None
Goal 5 Result: Completed
Goal 6 Statement: Review similarities and differences in the applicability of the methods for modeling and analyzing a time series based on temporal patterns (trend time series, seasonal time series, and cyclic time series)
Goal 6 Plan: Read source materials
Goal 6 Work Product: None
Goal 6 Result: Completed
Goal 7 Statement: Review similarities and differences in the applicability of the methods for modeling and analyzing a time series based on data composition (additive time series and multiplicative time series)
Goal 7 Plan: Read source materials
Goal 7 Work Product: None
Goal 7 Result: Completed
Goal 8 Statement: Review similarities and differences in the applicability of the methods for modeling and analyzing a time series based on time index (regular time series and irregular (i.e., random) time series)
Goal 8 Plan: Read source materials
Goal 8 Work Product: None
Goal 8 Result: Completed
Goal 9 Statement: Review similarities and differences in the applicability of the methods for modeling and analyzing a time series based on measurement scale (continuous time series and discrete time series)
Goal 9 Plan: Read source materials
Goal 9 Work Product: None
Goal 9 Result: Completed
Goal 10 Statement: Review similarities and differences in the applicability of the methods for modeling and analyzing a time series with multiple categorical attributes (number of variables, stationarity, temporal patterns, data composition, time index, and/ or measurement scale)
Goal 10 Plan: Read source materials
Goal 10 Work Product: None
Goal 10 Result: Completed
Part 7 of 9
Goal 1 Statement: Review similarities and differences in the applicability of the statistical methods for modeling and analyzing types of time series (moving averages, autoregressive (AR) models, moving average (MA) models, autoregressive integrated moving average (ARIMA), seasonal autoregressive integrated moving average (SARIMA), exponential smoothing, state-space models, and decomposition)
Goal 1 Plan: Read source materials
Goal 1 Work Product: None
Goal 1 Result: Completed
Goal 2 Statement: Review similarities and differences in the applicability of the machine learning methods for modeling and analyzing types of time series (random forests, gradient boosting, and support vector machines (SVMs))
Goal 2 Plan: Read source materials
Goal 2 Work Product: None
Goal 2 Result: Completed
Goal 3 Statement: Review similarities and differences in the applicability of the deep learning methods for modeling and analyzing types of time series (recurrent neural networks (RNNs), convolutional neural networks (CNNs), transformers, sequence-to-sequence (Seq2Seq) models, temporal convolutional networks (TCNs), autoencoders, and neural prophet)
Goal 3 Plan: Read source materials
Goal 3 Work Product: None
Goal 3 Result: Completed
Goal 4 Statement: Review similarities and differences in the applicability of the frequency domain methods for modeling and analyzing types of time series (Fourier transform, wavelet transform, and spectral analysis)
Goal 4 Plan: Read source materials
Goal 4 Work Product: None
Goal 4 Result: Completed
Goal 5 Statement: Review similarities and differences in the applicability of the probabilistic and Bayesian methods for modeling and analyzing types of time series (Hidden Markov Models (HMMs), Gaussian Processes, and Bayesian Structural Time Series (BSTS))
Goal 5 Plan: Read source materials
Goal 5 Work Product: None
Goal 5 Result: Completed
Goal 6 Statement: Review similarities and differences in the applicability of the anomaly detection methods for modeling and analyzing types of time series (threshold-based methods, z-score analysis, clustering-based methods, and isolation forests)
Goal 6 Plan: Read source materials
Goal 6 Work Product: None
Goal 6 Result: Completed
Goal 7 Statement: Review similarities and differences in the applicability of the feature engineering and preprocessing methods for modeling and analyzing types of time series (lagged features, differencing, detrending and deseasonalizing, normalization/standardization, and rolling statistics)
Goal 7 Plan: Read source materials
Goal 7 Work Product: None
Goal 7 Result: Completed
Goal 8 Statement: Review similarities and differences in the applicability of the hybrid and ensemble methods for modeling and analyzing types of time series
Goal 8 Plan: Read source materials
Goal 8 Work Product: None
Goal 8 Result: Completed
Goal 9 Statement: Review similarities and differences in the applicability of the visualization and exploratory methods for modeling and analyzing types of time series (time plots, autocorrelation function (ACF), partial autocorrelation function (PACF), seasonal plots, and box plots)
Goal 9 Plan: Read source materials
Goal 9 Work Product: None
Goal 9 Result: Completed
Part 8 of 9
Goal 1 Statement: Review definition, concepts, notation, terminology, components, and properties of combinatorics
Goal 1 Plan: Read source materials
Goal 1 Work Product: None
Goal 1 Result: Completed
Goal 2 Statement: Review definition, concepts, notation, terminology, components, and properties of probability sampling
Goal 2 Plan: Read source materials
Goal 2 Work Product: None
Goal 2 Result: Completed
Goal 3 Statement: Review definition, concepts, notation, terminology, components, and properties of non-probability sampling
Goal 3 Plan: Read source materials
Goal 3 Work Product: None
Goal 3 Result: Completed
Goal 4 Statement: Review similarities and differences in the applicability of the types of probability and non-probability sampling
Goal 4 Plan: Read source materials
Goal 4 Work Product: None
Goal 4 Result: Completed
Goal 5 Statement: Review procedures for using combinatorics for sampling techniques
Goal 5 Plan: Read source materials
Goal 5 Work Product: None
Goal 5 Result: Completed
Goal 6 Statement: Review how computers are utilized for performing the tasks and calculations for using combinatorics for sampling techniques
Goal 6 Plan: Read source materials
Goal 6 Work Product: None
Goal 6 Result: Completed
Part 9 of 9
Goal 1 Statement: Review definition, concepts, notation, terminology, components, and properties of linear recursive sequences
Goal 1 Plan: Read source materials
Goal 1 Work Product: None
Goal 1 Result: Completed
Goal 2 Statement: Review definition, concepts, notation, terminology, components, and properties of non-linear recursive sequences
Goal 2 Plan: Read source materials
Goal 2 Work Product: None
Goal 2 Result: Completed
Goal 3 Statement: Review procedures for methods for analyzing linear recursive sequences (iterative computation, explicit formula derivation, characteristic equation method, generating functions, dynamic programming, and memoization)
Goal 3 Plan: Read source materials
Goal 3 Work Product: None
Goal 3 Result: Completed
Goal 4 Statement: Review procedures for methods for analyzing non-linear recursive sequences (iterative computation, generating functions, dynamic programming, and memoization)
Goal 4 Plan: Read source materials
Goal 4 Work Product: None
Goal 4 Result: Completed
Goal 5 Statement: Review how computers are utilized for performing the tasks and calculations for analyzing linear and non-linear recursive sequences
Goal 5 Plan: Read source materials
Goal 5 Work Product: None
Goal 5 Result: Completed
Insights By Scott Ernst 