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Instructor: Dr. Shieu-Hong Lin
Class: T R
About weekly progress report: (Download this template)
By Thursday each week, you should spend around 5~10
minutes to
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Week 1. Could the computer ¡§act¡¨ or ¡§think¡¨ like
human beings? Progress report due Thursday, Sept. 4.
HAL and the
Construction of Artificial Intelligence.
Homework #1: Reflection on
AI due Thursday, Sept. 4.
To what extent, do you think the imagination in Stanley Kubrick¡¦s ¡§2001: a space odyssey¡¨ has been or has NOT been realized? Do you think the relationship between the software (computer programs) and the hardware of a computer is very much like the relationship between the mind and the body of a human being? Why or why not? Do you agree or disagree with them some of the sample reflections quoted here. Put down at least 500 words of your opinions (For example, could the computer software such as the chatter-bot in this lab and the intelligent chess programs implement rationality, emotion, unpredictability, or will to some extent? Are the human genomic sequences to some extent like the ¡§executable code¡¨ in control of human behavior? ) Include your opinions into the accumulative progress report when you email me your accumulative progress report.
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Week 2. Speech recognition and Hidden Markov models (HMMs). Progress report due Thursday, Sept. 11.
Homework #2: An
automatic game-playing agent due Thursday, Sept. 11.
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Week 3. More on HMMs and introduction to machine learning. Progress report due Thursday, Sept. 18.
Homework #3: First,
read and understand the application of HMMs in the
foreign traveler scenario depicted in the homework and
the use of the Viterbi-like algorithm to calculate key
probabilistic information as demonstrated in the homework. Then use the
algorithms to answer three questions asked in the homework. Due Thursday, Sept. 18.
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Week 4. Machine learning & decision tree induction + more on search. Progress report due Thursday, Sept. 25.
Homework #4: (i) For each document d in {A, B, C, D, E, F, G} and each person p
in {Johnny,
Winnie, Manny, and Cathy}, calculate
the logarithm of the probability Pr(d/p) that document d
is the resulting text when person p types the Biola
vision text. (ii) Based on the probabilities calculated in (i) above, for each document d determine the
top 2 most likely persons that may have generated the document d.
Due Thursday, Sept. 25.
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Week 5. Machine learning: Overfitting and performance evaluation. Progress report due Thursday, Oct. 2.
Homework #5: From the
exercises in Chapter 2, Eddie: exercises 4X,
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Week 6. More on classification and machine learning. Progress report due Thursday, Oct. 9.
Homework #6: From the
exercises in Chapter 4, Eddie: exercises 4X,
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Week 7. Machine learning and its applications. Progress report due Thursday, Oct. 16.
Homework #7: Turn in a
summary of the assigned papers. Discussion: Tuesday,
Oct. 14 and Thursday, Oct. 16.
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Week 8. Torrey
Conference & the Midterm
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Weeks 9~10. Basics of intelligent agents and knowledge representation.
Data mining project assignment 2: Do and report your progress in the following tasks. (i) Use the WEKA explorer to conduct classification experiments using clarifiers such as various Bayes classifiers, nearest-neighbor classifiers, rule-based classifiers, and decision tree classifiers. Try to balance the number of not-retained cases in the training dataset by duplicating them to bump the number of such cases up. (ii) What are the accuracies of the classification evaluated based on cross validation, or based on the hold-out scheme, or based on the test dataset? Are there problems of overfitting? If so, what are the evidences? Due: Thursday, Nov. 13.
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Weeks 11~13. More on Logic, knowledge representation,
and intelligent agents; Thanksgiving recess.
Data mining project assignment 3: Based on what you have done so far, develop a mini manual describing the whole procedure of (i) cleaning up the raw data set as an Excel worksheet, (ii) selection of attributes (i..e. removal of some columns in the Excel worksheet) , (iii) transforming it into an appropriate file in the arff format file, (iv) building a classification model using at least one of the classifiers provided by WEKA, (v) the steps for applying the model to classify new ceases, and (vi) a summary of the empirical results you have seen. Due Tuesday, Nov. 25.
Homework #8: Linear Regression. Due Thursday, Dec. 4.
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Weeks 14~15. Association analysis and Clustering
Take-home
exam on knowledge representation and automatic reasoning
Discussion of chapter 6 and chapter
8: Week 15.
Data mining project report: Wrap
up your data mining project assignments 1~3 as a report.
Answers to Homework#4: here. (Congratulations to Eddie for the 100% accuracy of prediction.)
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