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Course Outline
Artificial Intelligence History
- Intelligent Agents
Problem Solving
- Solving Problems by Searching
- Beyond Classical Search
- Adversarial Search
- Constraint Satisfaction Problems
Knowledge and Reasoning
- Logical Agents
- First-Order Logic
- Inference in First-Order Logic
- Classical Planning
- Planning and Acting in the Real World
- Knowledge Representation
Uncertain Knowledge and Reasoning
- Quantifying Uncertainty
- Probabilistic Reasoning
- Probabilistic Reasoning over Time
- Making Simple Decisions
- Making Complex Decisions
Learning
- Learning from Examples
- Knowledge in Learning
- Learning Probabilistic Models
- Reinforcement Learning
Communicating, Perceiving, and Acting;
- Natural Language Processing
- Natural Language for Communication
- Perception
- Robotics
Conclusions
- Philosophical Foundations
- AI: The Present and Future
Requirements
General knowledge of computing, biology, mathematics and physics
Testimonials
It felt like we were going through directly relevant information at a good pace (i.e. no filler material)
Maggie Webb - Margaret Elizabeth Webb, Department of Jobs, Regions, and Precincts
Really simple, easy to follow explanations Covered everything necessary in enough detail to understand fully, but so that it was not overwhelming good mix of theory and practice
Margaret Elizabeth Webb, Department of Jobs, Regions, and Precincts
the interactive part, tailored to our specific needs
Thomas Stocker
Ann created a great environment to ask questions and learn. We had a lot of fun and also learned a lot at the same time.
Gudrun Bickelq
Graphs in R :)))
Faculty of Economics and Business Zagreb
We gained some knowledge about NN in general, and what was the most interesting for me were the new types of NN that are popular nowadays.
Tea Poklepovic
new insights in deep machine learning
Josip Arneric
Working from first principles in a focused way, and moving to applying case studies within the same day
Maggie Webb - Margaret Elizabeth Webb, Department of Jobs, Regions, and Precincts
It was very interactive and more relaxed and informal than expected. We covered lots of topics in the time and the trainer was always receptive to talking more in detail or more generally about the topics and how they were related. I feel the training has given me the tools to continue learning as opposed to it being a one off session where learning stops once you've finished which is very important given the scale and complexity of the topic.
Jonathan Blease
Topic selection. Style of training. Practice orientation
Commerzbank AG
Given outlook of the technology: what technology/process might become more important in the future; see, what the technology can be used for
Commerzbank AG
I liked the opportunities to ask questions and get more in depth explanations of the theory.
Sharon Ruane
Very good all round overview.Good background into why Tensorflow operates as it does.
Kieran Conboy
I was amazed at the standard of this class - I would say that it was university standard.
David Relihan
Knowledgeable trainer
Sridhar Voorakkara
I really appreciated the crystal clear answers of Chris to our questions.
Léo Dubus
Abhi always made sure we were following along. Good mix of practice and theory.
Margaret Elizabeth Webb, Department of Jobs, Regions, and Precincts
The informal exchanges we had during the lectures really helped me deepen my understanding of the subject
- Explore
The visualisations were popular. I think they inspired some attendees to have more interest in the subject. It was also clear that the trainer knew a lot about the subject.
ARM Ltd.
Working with real industry-leading ML tools, real datasets and being able to consult with a very experienced data scientist.
Zakład Usługowy Hakoman Andrzej Cybulski
That it was applying real company data. Trainer had a very good approach by making trainees participate and compete
Jimena Esquivel - Zakład Usługowy Hakoman Andrzej Cybulski
The trainer was a professional in the subject field and related theory with application excellently
Fahad Malalla - Tatweer Petroleum
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