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Mini-assessment 2 paper with sample solutions now available -- 19 March 2013
| Term | Week | Day | Time | Type | Who | Topic | Links | Reading |
|---|---|---|---|---|---|---|---|---|
| Spr | 2 | Mon |
10:15 | Lecture | DLK |
Intro + Problem Representation |
Watch the Lighthill debate (1973) link
Watch BigDog robot video (2008) link |
Read Sect. 3.1-3.2 of AIMA Lecture: Introduction Lecture: Problem Representation |
| Spr | 2 | Mon |
14:15 | Lecture | DLK | Problem Representation | Lecture 2: Problem Representation (cont.) | |
| Spr | 2 |
Fri |
09:15 | Lecture | DLK | Uninformed Search (1) |
|
Read Sect. 3.3-3.4 Uninformed Search |
| Spr | 3 | Mon | 10:15 | Lecture | DLK | Uninformed Search (2) | BF Search code |
Read Sect. 3.5-3.6 Uninformed Search (cont.) |
| Spr | 3 | Mon |
14:15 | Lecture | DLK | Informed Search | Read Ch.4 Informed Search* (updated, 27 Jan 2013) |
|
| Spr | 3 | Tue | 10:15 | Practical | DLK | Representation and uninformed search | Uninformed search online demo | Handout pdf Handout ppt Sample Q: Search [Answer] |
| Spr | 3 |
Fri |
09:15 | Lecture | DLK | Informed Search | Problem sheet | (cont.) |
| Spr | 4 | Mon |
10:15 | Lecture | DLK | Local Search | Problem sheet - answers (available now) | Part 1: [PDF | Handouts] Part 2: [Slides] |
| Spr | 4 | Mon |
14:15 | Lecture | DLK | Implementations of search | |
Lecture: Implementing search |
| Spr | 4 | Tue | 10:15 | Practical | DLK | Informed Search and GA | Most answers Ex.1: A* search answer |
Handouts v2.0 [PDF] |
| Spr | 4 | Fri | 09:15 | Lecture | JC | Propositional logic: syntax and semantics | 6-up slides | |
| Spr | 5 |
Mon |
10:15 | Lecture | JC | Representing problems with logic | 6-up slides | AIMA 7.1-7.5 |
| Spr | 5 | Mon |
14:15 | Lecture | JC | SAT and the DPLL algorithm | AIMA 7.6.1 | |
| Spr | 5 | Tue | 10:15 | Assessment | DLK | First mini-exam |
A sample paper with answers |
2013 exam paper with sample answers |
| Spr | 5 | Fri |
09:15 | Lecture | JC | SAT solvers and local search | UBCSAT | AIMA 7.6.2-7.6.3 |
| Spr | 6 |
Mon |
10:15 | Lecture | JC | First-order logic: syntax and semantics | 6-up slides | AIMA 8.1-8.3 |
| Spr | 6 | Mon |
14:15 | Lecture | JC | Reasoning with first-order logic | 6-up slides | |
| Spr | 6 | Tue | 10:15 | Practical | JC | Logic Practical | ||
| Spr |
6 |
Fri |
09:15 |
Lecture |
JC |
Reasoning with first-order logic | ||
| Spr | 7 |
Mon |
10:15 | Lecture | SKM | KR 1 - Introduction to KR and the
Semantic Web |
4-up slides |
DGSW 1, SWWO 1-2 |
| Spr | 7 |
Mon |
14:15 | Lecture | SKM | KR 2 - RDF Tiny Camera example in RDF/Turtle Visualisation of same example using GraphViz |
4-up slides |
DGSW 2, SWWO 3 |
| Spr | 7 | Tue | 10:15 | Practical | SKM | KR - Practical 1 (RDF) |
Practical 1 Solution Sheet |
Michael_Jackson.rdf.turtle Michael_Jackson.rdf.dot Janet_Jackson.rdf.turtle Janet_Jackson.rdf.dot Michael+Janet_Jackson.rdf.turtle Michael+Janet_Jackson.rdf.dot |
| Spr | 7 | Fri |
09:15 | Lecture | SKM | KR 3 - RDFS Camera example in RDFS/Turtle Visualisation of same example using GraphViz |
4-up slides | DGSW 4, SWWO 6 |
| Spr | 8 |
Mon | 10:15 | Lecture | SKM | KR 4 - OWL Camera example in OWL/Turtle |
4-up slides |
DGSW 5, SWWO 9-11 |
| Spr | 8 |
Mon | 14:15 | Lecture | SKM | KR 5 & 6 - DL Syntax
and Semantics |
4up-slides |
Description Logic
Primer is a good start. But lecture slides cover more material. |
| Spr | 8 | Tue | 10:15 | Practical | SKM | KR - Practical 2 (OWL) |
Practical 2 Solution Notes | family.owl.turtle |
| Spr | 8 | Fri |
09:15 | Lecture | SKM | KR 6 - DL Syntax and Semantics (continues) | ||
| Spr | 9 |
Mon |
10:15 | Lecture | SKM | KR 7 - DL Reasoning | 4up-slides | For more in-depth DL reasoning the paper on the ALC DL is fairly close to the DL Reasoning slides. The BabyOWL DL is more expressive than the ALC DL described in the paper. |
| Spr | 9 |
Mon | 14:15 | Lecture | SKM | KR 8 - DL Reasoning continues |
||
| Spr | 9 | Tue | 10:15 | Assessment | SKM | Mini-assessment
instructions KR Assessment guide |
Sample Mini-exam paper with Solutions |
Mini-exam paper with Solutions |
| Spr | 9 | Fri |
09:15 | Lecture | SKM | KR 9 - DL Reasoning continues |
||
| Spr | 10 |
Mon |
10:15 | Lecture | SKM | KR 10 - DL Reasoning continues KR 11 - Information Retrieval |
4up-slides |
IIR (Chapters1,2,6) (free download) |
| Spr | 10 |
Mon |
14:15 | Lecture | DLK | Machine Learning: Introduction; Concept Learning | Concept Learning |
|
| Spr | 10 | Tue | 10:15 | Practical | DLK | Concept Learning |
Concept
learning Data set |
|
| Spr |
10 |
Fri |
09:15 |
Lecture |
DLK |
Concept Learning |
||
| Sum |
1 |
Tue |
14:15 | Lecture | DLK | Decision tree learning | slides | |
| Sum | 1 |
Thu |
09:15 | Lecture | DLK | Decision tree learning | slides Information Gain example |
|
| Sum |
1 |
Thu |
10:15 & 16:15 |
Practical |
DLK |
Decision tree learning | Instructions | |
| Sum | 1 |
Fri | 09:15 | Lecture | DLK | Decision tree learning | Revision handouts | |
| Sum | 2 |
Tue | 14:15 | Lecture | JC | Probabilistic reasoning | 6-up slides | AIMA 13 |
| Sum | 2 |
Thu | 09:15 | Lecture | JC | Probabilistic reasoning | 6-up slides | AIMA 13 |
| Sum | 2 |
Thu | 10:15 | Practical | JC | Probability Practical | Answers | |
| Sum | 2 |
Fri | 09:15 | Lecture | JC | Naive Bayes and maximum likelihood estimation | AIMA 20.2.1-20.2.2 | |
| Sum | 3 |
Tue |
14:15 | Lecture | JC | Bayesian machine learning: parameter estimation | Bayesian updating Perl script | AIMA 20.2.4 |
| Sum | 3 |
Thu | 09:15 | Lecture | JC | Bayesian machine learning: learning structure | Learning from observations, Statistical learning | AIMA 20.1 |
| Sum | 3 |
Thu | 10:15 | Practical | JC | Naive Bayes with R | ||
| Sum | 3 |
Fri | 09:15 | Lecture | JC | Computational aspects of Bayesian machine learning | ||
| Sum | 4 | Thu | 10:15 | Assessment | JC | Probabilistic reasoning and naive Bayes |
Last modified: Thu May 9 09:10:46 BST 2013