By Prof. G. Srinivasan | IIT Madras This course is aimed at being a pre term or a preparatory course for probability and statistics. Course description. - Definition & Options, Lesson 2 - Mean, Median & Mode: Measures of Central Tendency, Lesson 4 - Calculating the Mean, Median, Mode & Range: Practice Problems, Lesson 5 - Visual Representations of a Data Set: Shape, Symmetry & Skewness, Lesson 6 - Unimodal & Bimodal Distributions: Definition & Examples, Lesson 7 - The Mean vs the Median: Differences & Uses, Lesson 8 - Spread in Data Sets: Definition & Example, Lesson 9 - Maximums, Minimums & Outliers in a Data Set, Lesson 10 - Quartiles & the Interquartile Range: Definition, Formulate & Examples, Lesson 11 - Finding Percentiles in a Data Set: Formula & Examples, Lesson 12 - Calculating the Standard Deviation, Lesson 13 - The Effect of Linear Transformations on Measures of Center & Spread, Lesson 14 - Population & Sample Variance: Definition, Formula & Examples, Lesson 15 - Ordering & Ranking Data: Process & Example, Lesson 1 - Mathematical Sets: Elements, Intersections & Unions, Lesson 2 - Events as Subsets of a Sample Space: Definition & Example, Lesson 3 - Probability of Simple, Compound and Complementary Events, Lesson 4 - Probability of Independent and Dependent Events, Lesson 5 - Probability of Independent Events: The 'At Least One' Rule, Lesson 6 - How to Calculate Simple Conditional Probabilities, Lesson 7 - The Relationship Between Conditional Probabilities & Independence, Lesson 8 - Using Two-Way Tables to Evaluate Independence, Lesson 9 - Applying Conditional Probability & Independence to Real Life Situations, Lesson 10 - The Addition Rule of Probability: Definition & Examples, Lesson 11 - The Multiplication Rule of Probability: Definition & Examples, Lesson 12 - Math Combinations: Formula and Example Problems, Lesson 13 - How to Calculate a Permutation, Lesson 14 - How to Calculate the Probability of Permutations, Lesson 15 - Relative Frequency & Classical Approaches to Probability, Lesson 1 - Random Variables: Definition, Types & Examples, Lesson 2 - Finding & Interpreting the Expected Value of a Discrete Random Variable, Lesson 3 - Developing Discrete Probability Distributions Theoretically & Finding Expected Values, Lesson 4 - Developing Discrete Probability Distributions Empirically & Finding Expected Values, Lesson 5 - Dice: Finding Expected Values of Games of Chance, Lesson 6 - Blackjack: Finding Expected Values of Games of Chance with Cards, Lesson 7 - Poker: Finding Expected Values of High Hands, Lesson 8 - Poker: Finding Expected Values of Low Hands, Lesson 9 - Lotteries: Finding Expected Values of Games of Chance, Lesson 10 - Comparing Game Strategies Using Expected Values: Process & Examples, Lesson 11 - How to Apply Discrete Probability Concepts to Problem Solving, Lesson 12 - Binomial Experiments: Definition, Characteristics & Examples, Lesson 13 - Finding Binomial Probabilities Using Formulas: Process & Examples, Lesson 14 - Practice Problems for Finding Binomial Probabilities Using Formulas, Lesson 15 - Finding Binomial Probabilities Using Tables, Lesson 16 - Mean & Standard Deviation of a Binomial Random Variable: Formula & Example, Lesson 17 - Solving Problems with Binomial Experiments: Steps & Example, Lesson 1 - Graphing Probability Distributions Associated with Random Variables, Lesson 2 - Finding & Interpreting the Expected Value of a Continuous Random Variable, Lesson 3 - Developing Continuous Probability Distributions Theoretically & Finding Expected Values, Lesson 4 - Probabilities as Areas of Geometric Regions: Definition & Examples, Lesson 5 - Normal Distribution: Definition, Properties, Characteristics & Example, Lesson 6 - Finding Z-Scores: Definition & Examples, Lesson 7 - Estimating Areas Under the Normal Curve Using Z-Scores, Lesson 8 - Estimating Population Percentages from Normal Distributions: The Empirical Rule & Examples, Lesson 9 - Using the Normal Distribution: Practice Problems, Lesson 10 - Using Normal Distribution to Approximate Binomial Probabilities, Lesson 11 - How to Apply Continuous Probability Concepts to Problem Solving, Lesson 1 - Creating & Interpreting Scatterplots: Process & Examples, Lesson 2 - Problem Solving Using Linear Regression: Steps & Examples, Lesson 3 - Analyzing Residuals: Process & Examples, Lesson 4 - Interpreting the Slope & Intercept of a Linear Model, Lesson 5 - The Correlation Coefficient: Definition, Formula & Example, Lesson 6 - The Correlation Coefficient: Practice Problems, Lesson 7 - How to Interpret Correlations in Research Results, Lesson 8 - Correlation vs. Causation: Differences & Definition, Lesson 9 - Interpreting Linear Relationships Using Data: Practice Problems, Lesson 10 - Transforming Nonlinear Data: Steps & Examples, Lesson 11 - Coefficient of Determination: Definition, Formula & Example, Lesson 12 - Pearson Correlation Coefficient: Formula, Example & Significance, Lesson 1 - Frequency & Relative Frequency Tables: Definition & Examples, Lesson 2 - Cumulative Frequency Tables: Definition, Uses & Examples, Lesson 3 - How to Calculate Percent Increase with Relative & Cumulative Frequency Tables, Lesson 4 - Creating & Reading Stem & Leaf Displays, Lesson 5 - Creating & Interpreting Histograms: Process & Examples, Lesson 6 - Creating & Interpreting Frequency Polygons: Process & Examples, Lesson 7 - Creating & Interpreting Dot Plots: Process & Examples, Lesson 8 - Creating & Interpreting Box Plots: Process & Examples, Lesson 9 - Understanding Bar Graphs and Pie Charts, Lesson 10 - Making Arguments & Predictions from Univariate Data, Lesson 11 - What is Bivariate Data? No prerequisites are needed for this course. Brief Introduction to Machine Learning (No Coding) Projects 2 and 4 will be completed in Discussion Board Forums 1 and 2 (MLO: A, B, E; TSQR 1, 2, 4). Computing confidence interval given the underlying distribution, Hypothesis testing methodology, Null-hypothesis, p-value, Hypothesis Testing Intution with coin toss example, K-S Test for similarity of two distributions, Resampling and Permutation test: another example, Data Preprocessing: Feature Normalisation, Data Preprocessing: Column Standardization, Alternative formulation of PCA: Distance minimization, Eigen values and Eigen vectors (PCA): Dimensionality reduction, PCA for Dimensionality Reduction and Visualization, PCA for dimensionality reduction (not-visualization), How to apply t-SNE and interpret its output, Interactive Interview Session on Data Analysis, Code Walkthrough: Seaborn module for plotting in AI/ML, Code Walkthrough: Live session on Basics of Linear Algebra for AI/ML, Code-Walkthrough: Probability and statistics- I, Code-Walkthrough: Probability and statistics-II, Code Walkthrough: Dimensionality Reduction for ML/AI.

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