"My Computer Science Degree Journey" by Martin

 Before talking about new material, I want to mention that after a review I was able to understand more about machine learning this week. This week was about Pandas, a Python library for manipulating data. I learned some functions on how to use Pandas to visualize and edit data. I also learned some statistics concepts. I have some understanding of them, but I had forgotten a lot, so I had trouble understanding the mean in a PDF. Topics like skew and standard deviation I still somewhat understand, but besides that, I’m probably going to review more, just like I did with machine learning last week. I’m also going to look for more Pandas tutorials.

 This week was an introduction week. The slides covered a review of Python and also a guide on how to install Anaconda, which is a distribution platform. In my case, I had never used Anaconda before or the Spyder IDE, but it was easy to use. I still haven’t used all the tools in Spyder, but I will probably use more of them when I work with Matplotlib. I worked and practiced with the NumPy library, which has functions to work with arrays faster. I had practiced it before, but honestly I forgot the syntax, so today I felt refreshed. I also learned about filtering, which was new for me because I had never worked with NumPy and boolean arrays before. For the machine learning part, I still have trouble understanding the training data. I get confused about what the data points are and whether they come from the Iris dataset or are created by the program. I also get more confused with the k-nearest neighbors algorithm, but I am still studying so I can understand it better.

 I always had trouble understanding what dynamic programming is. I used to think it was just a function or something similar, but I think I understand it better now. It’s more like a strategy to solve problems, and this approach is used in algorithms such as Bellman-Ford or Floyd-Warshall.

 This week was about AVL trees, 2-3 trees, hashing, heaps, and heapsort. Something new I learned was the 2-3 tree. I had already seen it in college, but I didn’t understand it back then. This week, I was finally able to understand how it works, and it seems much simpler than I thought before.

 This week was about Kahn’s algorithm, decrease and conquer, binary tree traversal and height, and the quicksort algorithm. I did some exercises on quicksort because I had trouble understanding the visualization of partitioning. I had practiced it before, but I think I had some problems. After this, I was able to understand it better, which helped me complete the homework.

 This week’s topic was Merge Sort and the Divide and Conquer algorithm. It was also the week of the midterm. I felt confident when I studied, but unfortunately, I didn’t get the grade I wanted. There were some questions I felt bad about failing. The first one was detecting the binary search tree, and the last one was about finding the fake coin among 20 coins and determining if it was heavier or lighter. I answered that it would take three weighings, but the correct answer was two because the question asked for the minimum number of weighings, not the guaranteed result.