Why don't offer complete courses on-line?
It looks like I'm not the only one believing that.
Over three months ago, Stanford University launched three online classes and I joined two of them:
Both courses are offered on-line for free and they cover very interesting subjects.
I was more interested in Machine Learning, which is just a branch of artificial intelligence, but I decided to attend also "Introduction to Artificial Intelligence" in order to have a wider idea of the whole artificial intelligence world.
Last week I completed the final assessments for both courses.
AI professors have already sent the certificate of accomplishment. I'm still waiting for the Machine Learning one.
Introduction to AI was really theoretical, and we covered the following topics:
- Overview of AI, Search
- Statistics, Uncertainty, and Bayes networks
- Machine Learning
- Logic and Planning
- Markov Decision Processes and Reinforcement Learning
- Hidden Markov Models and Filters
- Adversarial and Advanced Planning
- Image Processing and Computer Vision
- Robotics and robot motion planning
- Natural Language Processing and Information Retrieval
Machine Leaning was really practical. We had programming exercises each week to implement using Octave, a high level, open source, interpreted language suitable for numerical computation . Below the topics we covered over the last few weeks:
- Introduction to Machine Learning
- Linear regression with one variable
- (Optional) Linear algebra review
- Linear regression with multiple variables
- Octave tutorial
- Logistic Regression
- One-vs-all Classification
- Regularization
- Neural Networks
- Backpropagation Algorithm
- Practical advise for applying learning algorithms
- How to develop and debug learning algorithms
- Feature and model design, setting up experiments
- Support Vector Machines (SVMs)
- Survey of other algorithms: Naive Bayes, Decision Trees, Boosting
- Unsupervised learning: Agglomerative clustering, k-Means, PCA
- Combining unsupervised and supervised learning.
- Independent component analysis
- Anomaly detection
- Other applications: Recommender systems. Learning to rank
- Large-scale/parallel machine learning and big data.
- Machine learning design / practical methods
- Team design of machine learning systems
I found both courses very interesting, I really learned a lot. While AI gave me very deep theoretical understanding of the whole artificial intelligence world, machine learning was very hands on, and I found many topics really applicable in my every day job.
How? I will talk about some of the topics I learned in the next few weeks.
In the meantime, Stanford has launched another series of online classes:
I'm planning to attend a few of them, and I strongly advise all my readers to enrol to a couple of them, at least.
I have only one suggestion to make to the Stanford University. It is about the assessments. How to prove that students are really following the Stanford Honour Code?
I think Stanford should host the examinations to other testing centres, like Prometric or others. This would give to the certificates of accomplishment more formal value and better industry recognition.
Stay tuned, in the next few days we will talk about Machine Learning and Artificial Intelligence and see if we can find applications in the business world.
Antonio
