Lecture 1 - Rules of probability
Lecture 2 - Discrete probability distribution
Lecture 3 - Continuous probability distribution
Lecture 4 - Moments: mean and variance
Lecture 5 - Moments: variance and covariance
Lecture 6 - Bayes theorem and likelihood
Lecture 7 - Concept of statistical tests
Lecture 8 - Vector and vector operations
Lecture 9 - Matrix and matrix operations
Lecture 10 - Determinant and Inverse of a matrix
Lecture 11 - Eigenvalue and eigenvector
Lecture 12 - Linear system of equations
Lecture 13 - Singular value decomposition
Lecture 14 - Getting ready with R
Lecture 15 - Algebraic and logical operations in R
Lecture 16 - Reading and writing data
Lecture 17 - Statistics using R - descriptive statistics
Lecture 18 - Statistics using R - t-test and ANOVA
Lecture 19 - Linear algebra using R
Lecture 20 - Scatter plot, Line plot and Bar plot
Lecture 21 - Histogram and Box plot
Lecture 22 - Heatmap and Volcano plot
Lecture 23 - Network visualization
Lecture 24 - Data visualization using ggplot2 - I
Lecture 25 - Data visualization using ggplot2 - II
Lecture 26 - Correlations
Lecture 27 - Linear regression - I
Lecture 28 - Linear regression - II
Lecture 29 - Linear regression using R
Lecture 30 - Multiple linear regression
Lecture 31 - Multiple linear regression using R
Lecture 32 - Nonlinear regression
Lecture 33 - Nonlinear regression using R
Lecture 34 - Clustering and classification
Lecture 35 - Logistic regression
Lecture 36 - Logistic regression using R
Lecture 37 - Distance mesaures for clustering
Lecture 38 - k-means clustering
Lecture 39 - k-means clustering using R
Lecture 40 - Hierarchical clustering
Lecture 41 - Hierarchical clustering using R
Lecture 42 - Decision tree classifier
Lecture 43 - Support vector machines
Lecture 44 - Higher-dimensional data in biology
Lecture 45 - Principle component analysis
Lecture 46 - Principle component analysis using R
Lecture 47 - t-SNE
Lecture 48 - t-SNE using R
Lecture 49 - Diffusion maps
