CST383 - Week 3

This week we learned about the topic of probabilities. We discussed naive probabilities, conditional probabilities, and random variables. We delved into Baye's Rule and worked through some examples. We also covered different types of distribution such as Bernoulli, binomial, normal/Gaussian, and uniform. We introduced some probability distribution functions such as PMF for discrete random variables, PDF for continuous random variables, and CDF for both. Lastly, we began to discuss machine learning and basic terms such as features and targets. The professor outlined for us the 4 most common problems: classification, regression, clusterin, and dimensionality reduction

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For the homework, I completed 4 labs, a coding assignment with 33 problems, and took a quiz that covered material from the previous weeks. For the coding questions, I got stuck on a few of them because my answers were different than the sample output provided by the professor. In the end, I think I was marked correctly and it was just due to small calculation differences. The quiz was a little more challenging than I expected, but I ended up with a 92 which I am happy about. I'm glad we were given two attempts so we could improve our scores.