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.
Audience
- Beginners interested in learning Artificial Intelligence, Machine Learning, and programming
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
Additional information regarding tool issues and imperfections.
Slawomir Gubala - Tech-Com sp. z o.o.
Course - OptaPlanner in Practice
Machine Translated
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.
Randy Comer Comer - Sandia National Labs
Course - Drools 7 and DSL for Business Analysts
Examples, Practical Applications, Answers to Questions
Rafal
Course - Wprowadzenie do Drools 7 dla programistów
Machine Translated