Machine Learning: Lecture Notes (1)

Here are some lecture notes from the second week of the online Stanford course in Machine Learning. I take the notes for myself as a form of rubberducking.

Terminology from week 1 (single feature linear regression):

• m is the number of training examples. 
• n is the number of features.   
• y is the output from the training set 
• x are the input features in the training set
• x⁽ⁱ⁾ are the input features of the i^th training set. 
• h_θ =>θ₀+θ₁x₁ + ... + θ_nx_n is the hypothesis function that will be modified to fit the training set.
• θ_j are the hypothesis values for each feature.
• J is the cost function. A common cost function is the mean square error function
• α is the learning rate

Multiple features:

It is common to add a constant feature to the model: θ₀, where x₀ = 1
Using matrices, h_θ (x) = θ^Tx
Gradient Descent Algorithm
For each θ_j: 
    Add the derivative of the cost function for that feature
    Add α times the average of the (hypothesis minus the actual observation) multiplied by that feature value.
Gradient Descent - Feature Scaling
The gradient descent algorithm can converge quicker if the feature range are scaled to the same interval.
Feature scaling is dividing the input variables by the range of the input variables. The new range will be 1, but the values can still be higher.

Mean normalization means subtracting the mean from each value. If you combine those methods, you get:

x_i := (x_i - mean(x))/range(x)

Normal Equation
This is a way to minimize J without iterations.

Gradient Descent or Normal Equation
GD: Need to chose a learning rate. NE: No need to choose a learning rate.
GD: Needs many iterations. NE: Needs no iterations
GD: O(kn^2). NE: O(n^3). NE is heavy for large feature sets!


The next step is a programming assignment where I'll implement linear regression to a simple data set. I won't publish that on my blog, however.

Machine Learning: Online Stanford Course

I have spent some time on programming this week - but not on my pet projects.

For obvious reasons, I can't elaborate on the programming I've done at work more that I have learnt a lot.

I've joined a dojo excercise on Machine Learning with an online course from Stanford/Coursera. We followed an example where we implemented a simple linear regression.

So far, I think the course is good for a beginner in AI. I seriously consider purchasing a course certificate when I finish that course.

StockReader: White and Black Lists

As I mentioned in earlier blog posts, I want to have a black list and a white list for combinations of expected name and returned names. Now, I got that code working.

An XML file lists all valid combinations (white list) and invalid combinations (black list). When the program is started, that XML file is opened and the lists are read into two dictionaries.

When the program finds a new stock record, it will query the class nameChecker for the combination of the expected name and the returned name. The result will be one of the following:

• Found in white list: The record can be added to the database
• Found in black list: The record is faulty and will be discarded.
• Found in neither white or black list: The combination needs to be assessed by the user manually. The record won't be added to the database. Instead, a proposal xml string is printed to the console when the scanning of records is done.

One of the few methods that is properly commented.
I really need to refactor my code.

The output can be copied into the XML file for the next iteration:

This means that there will be a lot of manual work initially. Once the lists are updated, unknown mismatches will be less frequent.

The current version of the web scraper builds the list of stocks dynamically and mismatches should be very unlikely.

Side Note: I'm focusing more on programming tasks at work. Therefore, I'll have a slower pace on my spare time projects and sometimes, I'll update the blog every second week, instead of every week.