This winter I’ve been teaching math with my dad (how lucky to get to try that!). In his teaching, he stresses the importance of reading. When we are young the majority of the math books are problems to solve. We learn that math is about solving problems. Later on, the textbooks change and become more and more text. This is when more people start having a problem with math. By only trying to solve the problems, first of all, the majority of the book is skipped (ever read one-third of a history book and expected to do well?). Secondly, it’s as hard as trying to read the text in a foreign language without learning the words first. Intuition can help to understand things, but there is no intuition for knowing that a right angle is 90° or that a rectangle has four right angles or what absolute value is. It’s just a concept to learn and memorize. I was teaching 12-year-old girl math and asked her what she did if she didn’t know or remember some math concept. She was not sure. Then I asked her what she did if she didn’t know some word in English. The answer was easy, look it up and repeat it 100 times!
“Look it up and repeat it 100 times!”
Being on the other side of the table, now teaching, has made me think a lot about how to study. I already wrote one article before about studying (Studying vs studying) so I had given this some thought but this time I am focusing more on math and the overall studying process, step by step. I gave a lecture about it to our students and they were happy about having this discussion. It also helped later on when I could remind them of this tip or that tool that would help them in some situations. Here is the summary of my thoughts:
I decided to divide the studying process into
READ -> UNDERSTAND -> REMEMBER -> USE
These steps don’t happen completely linearly, they mix but what is sure is that you have to start by reading(or hearing) the material and the main goal is to be able to use it. For each step, I will give a few points and thoughts on how to make it easier and more effective.
Computer Science is a very practical field and I like the emphasis my university places on teaching practical computer science skills. However, I recommend every computer science student planning on higher education to try to get some research experience during their undergraduate studies. First, it is helpful to know already at undergraduate level whether you enjoy research, for example to be able to choose appropriate higher education. Second, doing research is a great learning experience that is very useful for future career in both the industry and academia.
This semester I worked on an independent research project in the field of Multimedia, Data Science and Machine Learning, with my friend Þórhildur. I want to share both the Computer Science knowledge and the soft skills I learned from the process, so that I can hopefully inspire others to take on a research project. (The idea of the research can be found in this post)
Multimedia is a data that consists of a combination of different content forms such as images, video, text, audio and interactive content. Multimedia collections are becoming a central information resource for a growing number of domains, which increases the need for fast and insightful multimedia analysis tools. Since today’s multimedia collections are very large and ever-growing, the tools also need to be applicable to large-scale data. For example, the data obtained from social media platforms is almost all multimedia, the largest publicly available multimedia collection compromises 100 million images from Yahoo Flickr, called YFCC100M. However, there are many much larger multimedia collections that are not publicly available, like Facebook’s over hundred billion images.
But what is the best way to extract knowledge and insight from multimedia collections? The dominant approach revolves around search. Search is suitable only for cases when the user has a clear information need and is able to formulate it as a precise query. However, often the analyst wants to explore the collection, looking for the question to ask, and structure or categorize the data herself. Thus, multimedia systems should support interactive, open-ended tasks where the objective is the analyst’s knowledge gain. Below is example of few domains where this kind of interactive multimedia learning is important: