Worth reading:
Advice for a Young Investigator by Santiago Ramon Y Cajal
The mediocre can be educated; geniuses educate themselves. [Condorcet]
See also:
The Beautiful Brain: The Drawings of Santiago Ramon y Cajal (Amazon.co.uk)
Educate Thyself
Effective Study Skills
This post summarises learnings from Dr Oakley’s MOOC on learning How to Learn (see References below for link) together with my own thoughts and reflections on that course together with my Systems Theory post-grad course currently being studied with the Open University and other current studies. Related posts can be found under the tag Learning About Learning.
In particular, I have taken time out from that post-grad module due to feeling really confused, fed up, bored and generally utterly demoralised by my continually unsuccessful attempts to get on with studying that module. Rather than beat myself up about it, I decided to just give myself some time off to go and study a different (albeit, connected) subject. Hopefully it will free up some mind-space and sparks some useful ideas to go towards my next assignment.
Chunking as a Learning Method
Chunking is studying information in small, accessible packages. These chunks are more easily assimilated, since the mind is better able to unite and fit them together into a unified whole. Chunking has been described as a ‘mental leap’ – taking pieces of a jigsaw puzzle and fitting them together to make a whole picture. [Read more…] about Effective Study Skills
Perception
Asked question: What else do I need [to study/learn] for AI?
Answer on waking: Perception
Thinking on this a little, I realise perception must be key bcs this is the route/mechanism by (or, gateway through) which one experiences the world (= other) & from this one can begin to sort, categorise, pattern-seek & understand.
A realisation stemming from this is: without perception there can be no cognition (which I say must also be vital for intelligence) bcs perception creates the stimuli which ‘feed’ the cognitive processes.
Another key realisation is: ‘discrimination’ (used in its true sense) is the basis of intelligence. If you can’t perceive difference then you can’t create categories thus you can’t compute anything about either ‘self wrt other’ or ‘other wrt self’ or even ‘other1 wrt other2’, & so on. [Read more…] about Perception
MLND Project: Model Evaluation & Validation
3e Project: Model evaluation & validation
Project details
DESCRIPTION:
- use Boston house price dataset to PREDICT selling price of a new/unseen home
PROCESS:
- EXPLORE data > obtain important FEATURES & DESCRIPTIVE statistics abt data
- Properly SPLIT dataset into TRAINING & TEST datasets
- DETERMINE suitable PERFORMANCE METRIC for evaluating the problem
- ANALYSE performance graphs for learning algorithm over varying TRAINING SET SIZES & with varying number of PARAMETERS
- CHOOSE OPTIMAL MODEL that best generalises unseen data
- TEST chosen optimal model on a NEW SAMPLE & COMPARE PREDICTED selling price to ACTUAL statistics
[Read more…] about MLND Project: Model Evaluation & Validation
Focussed vs. Diffuse Thinking
Some reading materials (highlighted = likely useful for TU812 TMA03):
Chapters 1-3 of A Mind for Numbers are especially helpful in providing helpful information and additional exercises related to the materials of Module 1.
Worthwhile Additional Popular Works
- Scott Young, “I was wrong about speed reading: Here are the facts,” January 2015. This excellent blog post nicely summarizes what is known in relation to speed reading.
- John Dunlosky, “Strengthening the Student Toolbox: Study Strategies to Boost Learning,” American Educator, Fall, 2013. This excellent, comprehensive article is written by one of the top researchers in learning.
- Michael Friedman, “Note-taking tools and tips,” (October 15, 2014), Harvard Initiative for Learning and Teaching. This article, and an article embedded within it, (“Notes on Note-Taking: Review of Research and Insights for Students and Instructors“), have very useful insights into how to improve your note taking.
Learning About Learning
A key theme of part three of the systems module TU812 is ‘learning about learning’, and this course on Coursera by Dr Barbara Oakley – which covers that from one angle – came highly recommended: http://www.coursera.org/learn/learning-how-to-learn. It’s subtitled ‘powerful tools to help you master tough subjects’, which is definitely the kind of help I need right now with this systems course. The MOOC course is, I believe, in the top 3 on Coursera, although it’s worth pointing out the focus is on helping you with mathematical or scientific subjects. Yet the ideas and study tips should be extendable across to more abstract and wordy subjects (the ones I struggle most with).
Two Types of Thinking: Focussed and Diffuse
If we understand something about how the brain works, we can develop and deploy strategies to help us learn more optimally. As humans we have two thinking modes – with Dr Oakley refers to as ‘focussed’ and ‘diffuse’. These are mental states where we’re, respectively, concentrating really hard on something (like reading & note-taking from a course text) or allowing our minds to wander and day-dream (which is, of course, my preferred mental state!). Another way to think of the two different states is that focussed is looking at the details (a church within the landscape), while diffuse is looking at the much bigger picture (drinking in the whole scene as far as the eye can see). [Read more…] about Learning About Learning
Thoughts on Learning Systems
It seems clear to me (after thinking about & studying both learning and learning systems recently – as well as participating in several, both online & through distance learning) that a proper learning system – as I envisage it & will create it – follows this basic process [stated as description of ALGORITHM(S) – note that this is in very rough note form only!]:
1. READ unstructured textual information & conduct CONCEPT or KEYWORD ANALYSIS > ie HIGHLIGHT key ideas/concepts as you go > this is dependent upon what is deemed RELEVANT at that TIME > this can come from a database of CONCEPTS’ aka keywords & key phrases, RANKED for RELEVANCY (needs to be seeded ie pre-populated by HUMAN but later can be BUILT UPON by AI thru RECURSIVE LEARNING); text is parsed, OCCURENCE of keywords & phrases counted & recorded, CONTEXT of key word/phrase occurrence is noted > basically the web search algo but applied to a specific knowledge domain