Mav vs Stanford - Week 3

Stanford is offering 3 courses online: Introduction to Databases, Introduction to Artificial Intelligence, Machine Learning. I plan on completing all the advanced tasks every week, and posting my thoughts / reflections every Sunday. This is week 2, Sunday October 23, 2011

This week was sort of a mess, as I was pretty busy at work, and I was away at Yosemite for the weekend (more on that later). Anyways, this is the week where I finally did things in advance, and still ran out of time.

Introduction to Databases

I still haven't watched any of the videos, I'm not sure how much longer this can keep up.

Quiz 1: Relational Algebra Exercises

You were given 4 tables and asked to select random things like favourite pizzas. The exercise is actually good in theory, but since the syntax of the language was so painful, it was hard not to hate it. Coming with no formal DB education, and only minor MySQL usage having to type in Relational Algebra was frustrating at best. The other flaw with this quiz is that you can simply select the required values, since the query isn't tested with other test cases after you submit. I suppose anyone who puts in that much effort wouldn't bother cheating here though...

1 Attempt: 9/9

Machine Learning

Moving into meatier content, with non-linear regressions and smoothing parameters (regularization).

Quiz 6: Logistic Regression

The unit was overall very interesting, showing an alternative method to train a classifier and have more accurate decision boundaries. I'm afarid that the details of the math are starting to get lost on me, but at least the main concepts are sticking.

2 Attempts: 4.5/5, 5/5

Quiz 7: Regularization

This quiz was pretty easy, but perhaps that's a function of this unit being a sort of extension of the previous unit.

1 Attempt: 5/5

Assignment 2: Logistic Regression

This assignment wasn't nearly as easy as the first one, and there were no bonus assignments (oh noes!) Quirks with Octave aside, solutions can still be done in under 10 lines each, usually less. I know the professor said you will get a better idea of the material when you implement it, but personally, I don't feel that that is the case.

This assignment would have taken much less time if it wasn't for a few things. First, I forgot about the sigmoid function completely, and wasted 5-10 minutes there. Next, when implementing it, I searched for the exponential function in documentation, and found the wrong one. I gave up and returned after an hour or so. Lastly, Octave indices start at 1, not 0.

Final: 100/100

Introduction to Artificial Intelligence

Previous week's homework: 91%

As I mentioned earlier, this week was a mess, and I elected to do this course last. By Thursday night I didn't do any of this course, and I figured I'd just put nothing here this week. However, this course is strange and off with the other 2 courses, with a Monday deadline. I still wouldn't have finished if not for the extra 1 day extension this week.

Unit 5: Machine Learning

Considering I'm taking another course called Machine Learning, I was expecting this to be an easy unit. It wasn't, and the content didn't match the other course at all. More things to learn, but I didn't have enough time to get through all the videos, skipping most of the question ones and trying to get through all the content. This course seems to focus a lot on probability, whereas the actual machine learning course is about regression.

Video Lecture Score: 62%

Homework 3

Not much to say here, same as usual.

What I've Learned

As a side effect of starting so early, I'm forgetting details of the my experience taking this course. This week's post felt pretty vague because of it, but hopefully I will blog as I go through the content this week.