lab 10

This picture isn't the greatest, but I've included the flowchart in my file to show more detail.

The difference between Shannon and Hartley is Shannon is a much more accurate estimate of information. Both are based on estimating the amount of energy in an information system, but Hartley's method does not take into account as much information as Shannon does when guessing. Shannon's method is, simply put, more certain.

The entropy between the three instructuors was interesting. I guess it makes sense that the less data there is the more likely the measure will be accurate.
Matic had a number for every letter grade, while the other two professors had zeros in at least one area. The more zeros the better, because Mussolini was the most certain.

lab 9

This picture best describes what I think of about inductive modeling.
I like the concept of inductive modeling. It's interesting how it relates so well to how our brains use the idea of modeling and categorization. Our minds basically run the same way computers do and vice versa, and just thinking about that is remarkable. I've read a little more into inductive modeling and the user interface intrigues me the most. Inductive modeling is so efficent when it comes to making data directly available.

I enjoyed doing lab 9. I briefly noted in my previous post that I have little experience with excel, so not only did I learn how to use data analysis, I became way more familiar with Excel itself. I didn't even have it downloaded on my computer before the lab, that's how much I never used it. Having a step by step guide folllowed by a file you do all by yourself was useful, too, because it was a personal challenge to see if I could do everything without looking at instructions.

Lab 8 Histogram

I enjoyed lab 8. It wasn't very difficult, but I've never really used excel so it was good to get familiar with the basics.

lab 7

This circuit is simply functioning to demonstrate a XOR relationship. The only way the circuit will function is if both inputs are in agreeance. For example, they have to both be false or both be negative to be true.

lab 6 post

Any decimal number can be converted to a binary number by dividing by zero. For this lab, we are given the decimal number 529. We divide it by 2 (hence the name binary) and get 264 with a remainder 1. That 1 is the last digit of the binary conversion of 529. You then divide the answer, 264, by 2 again, getting 132 with a remainder 0. 0 is the second to last digit in the conversion. I keep diving my answer by 2 until i get zero as my answer. The complete binary number is 1000010001.

We can also convert binary numbers to decimal numbers. For this lab, we are given the binary number 110010101. First I wrote all the binary position powers of each digit beginning with the one farthest to the right. it's power is 2 to the 0. the next digit, 0, is 2 to the first power. the numbers increase as i go left. then i convert all the powers. then i multiply the powers by the digit in that place. For example, the first one with the power 2 to the 0, is 1x1 which equals one. the places with zeros can be ignored since zero cancels anything out. I then add the sum of the multiplication together to get my answer. The decimal for of this binary number is 405. hurray.

Positional number systems are systems where the position of the digit is based on a base number. In decimal numbers, for example, the base is a power of ten. the first position is 10 to the 0, the second is 10 to the 1, and so on. Binary numbers follow the positional number form as well. In non-positional number systems, the positions are of no significance. Integers are used to carry the meaning.

Lab 5

Unix Commands
The 'whoami' command was one of the commands I learned from lab 5. I think it's important because it confirms who you are logged in as. To go a step further, the 'finger' command, really lets you know that the computer knows who you are. It was fascinating to see all of my information come up that I didn't even know the computer knew. I am not sure if either of these has a counterpart.
'cd' was probably the most important command I learned. It allows you go back to your home directory from where you are. It helped if I messed something up I could just restart in a way. I am pretty sure 'cd' has a counterpart. 'mkdir' was another important command. You can make any directory you want with this command. From there you can put things in files under other commands. 'mkdir' has an equivalent DOS counterpart as well.

Chapter 6
The topic of Chapter 6 of Andy Clark's book was Global Swarming. The introduction analogy of the slug was interesting and sensible. I have experienced the "slug trail" numerous times on the Internet- I am probably the biggest supporter of shopping online instead of going out. And I noticed the feature of "Those who bought this item also bought/looked at.." when it first was introduced and I like the idea behind it. I can't say I've ever bought anything because of it, but I've definitely wasted extra time browsing. Collaborative filtering is an amazing concept too, when you think about it. It's like there is a big chalk board keeping tallies on what consumers buy item A with. Through this method they are able to figure out which items to put on the "If you like this item, you might also like..." list. The best part about these features, is you don't even have to use them. They are really designed to get you to spend more money. It's still has kinks that I think it needs to get worked out. For example, when you sign into amazon, it immediately gives you "personalized recommendations" which are actually just things you looked at before or already bought. It gets really old. Pages and pages of recommendation, depending on how often you shop, which for me is often. You can click 'not interested' on the item, but I think they need to let you clear all previous "trails" if you want.
Overall, though, collaborative filtering and recommendations are a good idea and are more useful than not. I'd give Amazon an A-.

Modeling

I enjoyed reading the modeling notes. I think the concept modeling is really interesting. I can understand it in general terms, but I can understand why it can get so confusing.
The example with 15 people trying to pick a beverage to serve made a really good point. Milk won by a show of hands yet those votes were not nearly as strong as the people that didn't raise their hand and wanted milk as a last resort. Yet then when water was chosen, it was brought up that there were more people that would've rather had soda. The example did a great job of showing how many ways you can look at things, and how electrical modelers work.