Artificial Intelligence 2

Following on from my question posted on 22 Jan - has anyone figured out how to do exercise 19.4 yet? In the "Inductive Learning with Inverse Deduction" section, it seems to imply that C1 /\ C2 --> C, yet exercise 19.4 has C = true implying C1 and other bits and pieces of clauses. I just don't get it? Is this syntax used anywhere else in the book, does anyone know?by

I have been studying this subject flat out for about a month and I keep approaching flat panic. I am just hoping for 50%. I have no idea how the paper is going to be presented and I know if it ends up being heavily based on chapter 21, I will probably have a hernia and keel over in the exam hall. Chap 21 is NOT my favourite chapter, nor is chapter 19. I am started feel a little bit more secure onby

I got 70% for my swarm intelligence tutorial - would be happy to swap answers with anyone else who got 60%+ for this tut? Must say, I only did a 2.5 page summary of all the main algorithms / history etc though and didn't get into any technical detail. I assume though that we are going to get a 20pt question saying "Discuss the principles and most important algorithms of Swarm Intelligeby

Anyone else started studying for this yet? Feels like I am shouting into the void hereby

I've been going over question 20.1 this weekend again (I remember breaking my head on this last time round as well aaargghh!). Once again, I find that the only way I can get my graphs to work out to anything sensible is if I "fudge" the value of alpha over and over again in order to make the sum of all the probabilities add up to one. Must say though, I don't like this verby

Is anyone else studying more than just chapters 18 - 21? I have summarised pieces of chapters 1, 7, 8, 9, 13, 14, 16 and 17, as well as chapter 18 - 20 (hoping to wrap up chapter 21 this week). I don't think I would ever have gotten my head around some parts of chapters 18-20 without having done this. I am planning to work through every exercise in chapters 18 - 21 as well, before the exaby

Did anyone manage to find any good books on Swarm Intelligence? If so, could you perhaps recommend them here? I am mainly using the internet to do Assignment 3, so if anyone has managed to track down better sources, it would be a help if you could list them.by

Nope, I haven't received any solutions or even my assignments back yet? Anyway else had any luck?by

Has anyone received the solution to Assignment 1 or any marks yet? I hope this isin't going to be yet another Unisa course where the lecturer loses / bins / burns your assignments and then on top of it couldn't be arsed to send out any solutions.by

hmm, I had a look at this and the best i can come up with is that the ML statistician would just take drug B (the most likely fix), but the Bayesian would take both drugs and wait for more data to reveal the true illness. In the second scenario, the ML probably wouldn't break disease B down into 2 seperate hypotheses (keeping it simple), but the Bayesian would probably split the hypotheseby

Will do! I'm giving it a crack tonight, will let you know how far i getby

Thanks for that! I have been stubbornly fighting this question and will now have to zip through all the rest of the assignmentby

Thanks a mil, I will give this a try!!by

ps. If anyone could give me a quick breakdown of how to calculate the first couple of values in Fig 20.1 (b), I will be more than happy to email on my Fig 20.1 (a) Excel spreadsheet. I'm not sure how correct it is, but somehow, whether it is by hook or by crook, the graphs are coming out beautifully, heck knows how!by

Glory halleluja, thank heavens I am not the only person scratching my head with this question I think I have cracked 20.1 (a), my graphs are correctly predicting the hypotheses for which I generated data. But, I am getting stuck on 20.1 (b), can't seem to make head or tail of this one Anyway, it's late and I am sick of this question, suspect I am going to boot it and move onto tby

I've done 1/2 of 20.1, but I am struggling with the generation of the Fig 20.1 (b) graphs. I just can't figure out how you would go from equation 20.2 to Fig 20.1 (b)? Does anyone know how the first 5 or so points are calculated in Fig 20.1 (b)? This question is driving me crazy!by

O dear, I am just as screwed. I'm currently sitting in london, I don't have the luxury of just trooping down to the local unisa office to register - it's web registration or nothing. Am I just imagining this or is UNISA actually getting worse and worse every year?by

yup, no results either here. did get COS461 however.by

i've also made chunks of summaries, but haven't practised any diagrams? what kind of diagrams are we talking about here, stuff like the iso networking model etc? i am just trying my best to understand all the minute detail, because even tho the textbook gives a very broad overview, the assignment questions all seem to expect you to have memorised every last fact in the book. 5 tenby

ya, i am lucky enough to have v1 of the book thank goodness! I bought it on amazon second hand, only way i could get it!by

Hi Thanks a mil for the offer, but I ended up just buying this article I worked through Assignment 3, Question 6 - I think the answers should be something like the following (if anyone sees anthing drastically wrong here, feel free to shout!): 6.1 sensitivity = the fraction of true positive results that are correctly classified as positive specificity = the fraction of true negative rby

i've received all my assignments back (and have points for all of them on myunisa), but i don't have any model answers unfortunately, don't think anyone has i'm still struggling to get hold of the "Evaluation of outcome prediction for a clinical diabetes database" article. I sent the lecturer an email months ago asking if there was anywhere we could download it (by