Learning AI by practice ( Perceptrons, Neural networks and Bayesian AI)

Question

I'm about to take a course in AI and I want to practice before. I'm using a book to learn the theory, but resources and concrete examples in any language to help with the practice would be amazing. Can anyone recommend me good sites or books with plenty of examples and tutorials ?

Thanks !

Edit: My course will deal with Perceptrons, Neural networks and Bayesian AI.

Answer 1

Really depends on what area you want to specialize on. There is the startup - resource : is here. I learned about neural nets from their example. Can you elaborate what kind of AI it should be? Ah and i forgot: this link is a very nice forum where you can look at problems other people have and learn from that. Cheers.

Answer 2

My advice would be to learn it by trying to implement the various types of learners yourself. See if you can find yourself a dataset related to some interest you have (sports, games, health, etc.) and then try and create a learner to do some kind of classification (predicting a winner in a sports game, learning how to classify backgammon positions, detecting cancer based on patient data, etc.) using different methods. Start with Decision Trees if that's part of your future course work since they're relatively simple, then move on to neural networks.

Answer 3

Here is a set of sources, each one of which i recommend highly--for the quality of the explanation, for the quality of the code, and for the 'completeness' of the algorithm demo.

• Least-Squares Regression (Python)

• k-means clustering (Python)

• Multi-Layer Perceptron (Python)

• Hopfield Network (Python)

• Decision Tree (ID3 & C4.5)

In addition, the excellent textbook Elements of Statistical Learning by Hastie, et al. is actually free to download. The authors have an R package that accompanies this textbook which contains all of the code. This book includes detailed discussion of most (if not all) of the major classes of ML algorithms, with specific examples that refer to working code and 'real-world' data.

Answer 4

Personally I would recommend this M.Tim.Jones book on AI.

Has many many topics on AI and almost every type of AI discussion is followed by C example code. Very pragmatic book on AI indeed !!