Course Outline
Probability (3.5h)
- Definition of probability
- Binomial distribution
- Everyday usage exercises
Statistics (10.5h)
- Descriptive Statistics
- Inferential Statistics
- Regression
- Logistic Regression
- Exercises
Introduction to Programming (3.5h)
- Procedural Programming
- Functional Programming
- OOP Programming
- Exercises (writing logic for a game of choice, e.g. noughts and crosses)
Machine Learning (10.5h)
- Classification
- Clustering
- Neural Networks
- Exercises (write AI for a computer game of choice)
Rules Engines and Expert Systems (7 hours)
- Intro to Rule Engines
- Write AI for the same game and combine solutions into hybrid approach
Requirements
None. All concepts like probability and statistics will be explained during this course. If you are already familiar with probability and statistics, please refer to our course code aiint.
Testimonials (5)
The pace was good, with a nice mixture of knowledge sharing, demonstrations and practical work. Filip was very engaging and provided the energy to get through the course. It was good that there was a lot of 1:1 tuition, with Filip going through individual training exercises.
Colin - Worldpay
Course - BPMN, DMN, and CMMN - OMG standards for process improvement
The training definitely backfilled some of the gaps in my knowledge left by reading the OptaPlanner userguide. It gave me a good broad understanding of how to approach using OptaPlanner in our projects going forward.
Terry Strachan - Exel Computer Systems plc
Course - OptaPlanner in Practice
Shared examples of every function and/or operators are all well explained.
Brian Amlon - Thakral One, Inc.
Course - Introduction to Drools 7 for Developers
a lot of practices are very welcome, many try and learn cases are embedded
Nadia Ivaniuk - Credit Suisse (Poland) Sp.z o.o.
Course - Modelling Decision and Rules with OMG DMN
Exercises and solving problems in groups when the problems were more difficult.