Pattern Recognition and Computing Power

Initially, it was very perplexing attempting to understand the intersection between statistics and machine learning, but this week’s materials have made this more clear. According to the CrashCourse videos assigned, one primary task of machine learning is to determine the most accurate “confusion matrix” for a given set of “labeled data.” (Machine Learning & Artificial Intelligence 2021) As more “features” are added to the matrix, the more complicated the algorithm or SVM (support vector machine) is required to determine the most accurate confusion matrix. However, what has also become clear, is while these machine learning methods are able to analyze large amounts of data and very accurately assign a confusion matrix, like medicine, this is still an imperfect science. (Alpaydin 58) No more is this evident than with the Karpathy article. 

In the Karpathy article, machine learning in relation to graphical interpretation is depicted with a database of selfies from (instagram?) a social media platform. The algorithm Karpathy used, known as t-SNE, would search selfies based on a certain set of parameters (or features) to filter what were deemed the “best” selfies. Karpathy 2015) Yet, these parameters were very limited and did not take into account the multitude of features which might culminate in what could be considered the “best.” For example, one of the parameters used when determining the quality of a selfie was the number of likes received, which is hugely subjective and does not take into account ratios of followers from male to female. Additionally, females on average interact most with other females on social media, whereas men on average are more likely to comment or like female posts. (Fowler 2017) This bias was apparent when the top 100 selfies determined by Karpathy’s algorithm were entirely female. This is likely indicative of an obstacle to overcome with machine learning, and the consideration of a multitude of feature extractions.  


In the Dougherty reading, classifications were broken down into supervised, unsupervised and Bayes decision theory. Each of these methods of classification maintained varying degrees of computing power throughout the process. My question concerns which method is the most efficient in regards to computing power? (Dougherty 19) Additionally, are the methods interchangeable or exclusive to only certain kinds of classification? 

In the Alpaydin reading, document categorization, bag of words and deep learning,  were all mentioned, and in particular in relation to social media metadata gathering. (Alpaydin 69-70) All three have been utilized in disinformation campaigns, but why is this same technology failing to halt disinformation campaigns which still ravage social media platforms? Lastly, in reference to handwritten characters, Alapydin said, “…there is still no computer program today that is as accurate as humans for this task.” (Alpaydin 58) Has this changed since 2016 when the book was written? Or is handwritten text still far from where it could be in accuracy and classification?   


Alpaydin, Ethem. Machine Learning: The New AI. MIT Press, 2016.

Dougherty, Geoff. Pattern Recognition and Classification: An Introduction. Springer, 2013.

Fowler, Danielle. “Women Are More Popular On Instagram Than Men According To New Study.” Grazia, Accessed 1 Mar. 2021.

Karpathy, Andrej. What a Deep Neural Network Thinks about Your #selfie. Accessed 1 Mar. 2021.

Machine Learning & Artificial Intelligence: Crash Course Computer Science #34., Accessed 1 Mar. 2021.