Much Needed Mathematics for Machine Learning Algorithms

“Much Needed Mathematics for Machine Learning Algorithms” by Kolla Leela Kishan


Math in Machine Learning

Certainly having a strong background in mathematics (eg. Linear Algebra, Multi-variables Calculus, Baeysian Probability, etc) will make it easier to understand machine learning at aconceptual level.

“If the math seems tough, focus on the practical first, learn through analogies and by building something yourself.

But if the math comes easy, you’re starting with a solid foundation.”

Machine Learning is Fun! – Adam Geitgey – Medium

(中文) :

Unsupervised learning is the future ML (Machine Learning) – of which AI is a branch – with the latest algorithm Deeplearning showing only 5% of its potential (more yet to be invented).

Singapore has recently launched an AI program to educate 10,000 students & workers. (Partnership with Microsoft and IBM, a 3-hour free lesson).

The world’s 4 AI gurus :

  1. (UK/Canada) Prof Geoffrey Hinton (*) , the inventor of DeepLearning, and
  2. his post-doctorate associate (France) Prof Yann Lecun ,
  3. The ex-Google & ex-Baidu AI Chief Prof Andrew NG 吴恩达,
  4. The AlphaGo creator Demis Hassabis

Andrew and Demis both studied in Singapore secondary schools (NG in Raffles Institution) before pursuing university in Stanford and Cambridge, respectively.

Note (*) : Prof Geoffrey Hinton was involved in the 80s Expert Systems where rule-based knowledge engine was the AI (2.0) . This AI failed because of fixed rules knowledge base under “supervised learning” from human domain experts, who each differed from another in opinions, to give an un-biased “weights” (rule probabilities from 0 to 1). Prof Hinton continued the AI research by moving from UK to Canada, where he developed the Deeplearning algorithm with unsupervised learning from Big Data Training feed to calculate the “Costs” (ie deviations of AI result versus actual result, using Cauchy’s Calculus eg. “Gradient Descent”, etc).