improving classification performance with mel frequency cepstral coefficients summary computer vision pattern recognition introducing image processing loading and displaying images basic image processing thresholding gaussian blurring filtering for different effects adding salt and pepper noise pattern recognition computing features from images writing your own features classifying harder dataset local feature representations summary putting the center in focus dimensionality reduction sketching our roadmap selecting features detecting redundant features using filters correlation mutual information asking the model about the features using wrappers other feature selection methods feature extraction about principal component analysis (pca limitations of pca and how lda can help multidimensional scaling (mdssummary sketching pca applying pca big(gerdata learning about big data using jug to break up your pipeline into tasks about tasks reusing partial results looking under the hood using jug for data analysis [