Intro to AI: Lecture 1 Volker Sorge Administrative Lecture Overview
Overview Volker Sorge http://www.cs.bham.ac.uk/~vxs/teaching/ai - - PowerPoint PPT Presentation
Overview Volker Sorge http://www.cs.bham.ac.uk/~vxs/teaching/ai - - PowerPoint PPT Presentation
Intro to AI: Lecture 1 Volker Sorge Administrative Lecture Overview Introduction to Artificial Intelligence Overview Volker Sorge http://www.cs.bham.ac.uk/~vxs/teaching/ai Intro to AI: Staff Lecture 1 Volker Sorge Administrative
Intro to AI: Lecture 1 Volker Sorge Administrative Lecture Overview
Staff
Dr Volker Sorge Office: 207 Tel: 43746 Email: V.Sorge@cs.bham.ac.uk Office hours: Monday, 10:30–11:30am URL: www.cs.bham.ac.uk/~vxs Plus a team of demonstrators.
Intro to AI: Lecture 1 Volker Sorge Administrative Lecture Overview
Lectures
Monday, 3pm–4pm Poynting Building, Large LT (S02) Thursday, 4pm–5pm Poynting Building, Large LT (S02) Building R13 on Campus Map
Intro to AI: Lecture 1 Volker Sorge Administrative Lecture Overview
Assessment
Exam: 80% of your course mark will be determined by a 1.5-hour examination in May (or early June). Continuous: 20% of the mark is determined by continuous assessment. Continuous assessment will (most likely) consist of a mid-term test and an assignment at the end of term. Resits One resit opportunity in September 2016 for eligible
- students. Please consult
http://www.cs.bham.ac.uk/internal/modules/resit.html
for eligibility criteria. Assessment is 100% by examination.
Intro to AI: Lecture 1 Volker Sorge Administrative Lecture Overview
Textbook
Artificial Intelligence: A Modern Approach (3rd edition) Stuart Russell & Peter Norvig Prentice Hall, 2012 The textbook will be accompanying reading material.
Intro to AI: Lecture 1 Volker Sorge Administrative Lecture Overview
Lecturing Approach
◮ Combination of lectures and self-study. ◮ Pre-reading is often required. ◮ Lectures aim to deepen understanding. ◮ Handouts will not cover everything. ◮ Take notes!
Intro to AI: Lecture 1 Volker Sorge Administrative Lecture Overview
Tentative Syllabus
◮ Search
◮ Uninformed search ◮ Informed search ◮ Adversarial search
◮ Knowledge and belief
◮ Representation ◮ Semantic Networks ◮ RDF
◮ Planning
◮ State-space planning ◮ Partial-order planning
◮ Probabilistic Reasoning
◮ Probabilities ◮ Bayesian networks
◮ Learning
◮ Unsupervised learning ◮ Supervised learning ◮ Reinforcement learning