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 Lecture Overview 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: Lectures Lecture 1 Volker Sorge Administrative Lecture Overview Monday, 3pm–4pm Poynting Building, Large LT (S02) Thursday, 4pm–5pm Poynting Building, Large LT (S02) Building R13 on Campus Map
Intro to AI: Assessment Lecture 1 Volker Sorge Administrative Exam: 80% of your course mark will be determined by Lecture Overview 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: Textbook Lecture 1 Volker Sorge Administrative Lecture Overview Artificial Intelligence: A Modern Approach (3rd edition) Stuart Russell & Peter Norvig Prentice Hall, 2012 The textbook will be accompanying reading material.
Intro to AI: Lecturing Approach Lecture 1 Volker Sorge Administrative Lecture Overview ◮ 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: Tentative Syllabus Lecture 1 ◮ Search Volker Sorge ◮ Uninformed search Administrative ◮ Informed search Lecture Overview ◮ 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 ◮ Everything else
Recommend
More recommend
