It’s week 3 and we’re onto something enriching and interesting. This week, we had RA Timothy sharing the basics of machine learning. We had a great turn out with close to 400 sign ups. Unfortunately we had to turn down a few as we could only accept about 80 due to the size of our venue. (maybe we will host another one, so stay tune).
The workshop was targeted at complete beginners and required absolutely no background in ML or CS. It was split into 2 parts — classroom style lesson and then a hands-on example. The lesson started with the introduction of the background, key applications and algorithms of machine learning.
We went through the basic algorithms such as linear/logistic regression, decision trees, neural network and support vector machines (SVM). The underlying mathematics and optimizing algorithms such as gradient descent and the kernel trick were explained as simply as possible, using as many diagrams and animation with as little mathematical notations and formulas as possible.
The second part was a hands-on practical lesson in which participants will apply the things that was taught in the first section and solve a machine learning problem. There was hands on coding and building of a predictive model. Below are some of the pictures for the night
We are glad to see so many people having a genuine interest in this relatively new field of machine learning. Do continue seeking, learning and asking. If there are additional queries, please feel free to approach Timothy, or ask any RAs if you cant find him.
We are thinking of hosting another one, so do feedback to the Nhouse RAs regarding interest and topics to cover.
Have a great week ahead and join us for week 4’s event
