Machine Learning and Data Analytics

CSCI 480, fall semester, 2018

 

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Instructor:                 Dr. Shieu-Hong Lin (Description: Description: Description: Description: Description: Description: Description: LinEmail)   Course Syllabus

 

Class:                        TR 12:00-13:15 pm at Busn 210

 

Office Hours:             Dr Lin (Lim 137): MW TR 3:00-5:00pm   email  Description: Description: Description: Description: Description: Description: Description: Description: Description: Description: Description: Description: Description: Description: Description: Description: Description: Description: Description: Description: LinEmail to confirm an appointment in advance

 

Submission of all your work: go to Biola Canvas               Your grades: see them under Biola Canvas

 

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Week 1. Overview of the Landscape of Machine Learning

 

Reading 1: Report due: Thursday, Sept. 13

 

 

Explorations:

 

Lab # 1 Rock-Paper-Scissor: Report due: Thursday, Sept. 13

1.      Collecting data: Download, unzip, and run rock-paper-scissor Agent#1 (or this alternative x64 executable) for a couple of times. Each time the program would require you to play with the agent for 100 matches and yield a transcript file RPS_transcript.txt about the outcomes of these 100 matches in the same folder. You can rename these text transcripts and then put them together into a single combined transcript file of all matches. What is percentage of matches in which you won? What is percentage of matches in which you lost?

2.      Learning from data: Try to learn from the results in the transcript of matches to improve your chance of winning the game. Then play with Agent #1 again based on what you have learned from the data in Step #1. Put down (i) what you have learned from the data and (ii) whether it did help you to improve the chance of winning the game into a WORD or text document.

3.      Submission of your work: Upload the combined transcript in Step #1 and the file of your thoughts and exploration in Step #2 file under canvas.

 

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Week 2. Basics of NumPy I + Concepts of Inputs for Data Mining

 

Reading 2: Report due: Thursday, Sept. 20  

 

Lab # 2 Rock-Paper-Scissor analysis using Python: Report due: Thursday, Sept. 20

4.      Loading data collected in Lab#1 into a Numpy array: Consider the combined transcript file of all matches in Lab #1 again. Use numpy.loadtext (example, documentation) to load the data into a numpy array.

5.      Exploring the data using Numpy: Try to apply some basic Numpy facilities you have learned from the reading to analyze the data. Put down what you have done in your explorations and your findings in a file.

6.      Submission of your work: Upload the combined transcript in Step #1 and the file of your thoughts and exploration in Step #2 file under canvas.

 

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Week 3. Basics of NumPy II + Data Mining and Knowledge Representation

 

Reading 3: Report due: Thursday, Sept. 27  

 

Quiz #1 on the basics of Numpy: Thursday, Sept. 27  

 

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Week 4. Basics of Pandas I + Data Mining and Knowledge Representation

 

Reading 4: Report due: Thursday, Oct. 4  

 

 

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Weeks 5-6. Basics of Pandas II + Supervised Learning + Torrey Conference

 

Reading 5-6: Report due: Thursday, Oct. 18  

 

Lab #3 (Supervised leaning for classification using WEKA): Thursday, Oct. 18  

 

Quiz #2 on the basics of Panda I & II: Thursday, Oct. 18  

 

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Week 7. More on Pandas + Naïve Bayes for Supervised Learning

 

Reading 7: Report due Thursday, Oct. 25  

 

 

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Week 8. Matplotlib + More on Naïve Bayes for Supervised Learning

 

Reading 8: Report due Thursday, Nov. 1  

 

Lab 4 (Naïve Bayes classification): Thursday, Nov. 1  

 

 

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Week 9. More on Matplotlib + Supervised Learning: Decision Trees

 

Reading 9: Thursday, Nov. 8

 

Lab 5 (Naïve Bayes classification): Thursday, Nov. 8  

 

Homework #1: (Decision tree induction based on entropy and information gain): Thursday, Nov. 8

 

 

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Week 10. Review of Matplotlib + Supervised Learning: Linear Model

 

Reading 10: Thursday, Nov. 15

 

Quiz#3: Due Thursday, Nov. 15

P        Create a Jupiter notebook with Python code completed as described in the 5 problems in Quiz#3 for using Pandas to work on the data set in this csv file in Lab 4 and Lab 5.

P         Open-book quiz, but no collaboration with others allowed.

 

 

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Weeks 11-12.  Supervised Learning: Linear Model  +  Neural Networks and Deep Learning 

 

Faith and Learning Integration Assignment on Creation and Computer Science due: Tuesday, Nov. 20

P         Dr. Lin will be out of town for a conference on Nov. 20. Please use the class time for reflection needed to do this assignment.

P         You should put down what you have in the reflection process according to the requirement in the assignment.

P         Submit your reflection report accordingly through Canvas.

 

Reading 11: Thursday, Nov. 22 (submission open till Nov. 27 without penalty)

 

Homework #2: (Linear Regression and Linear Models):  Thursday, Nov. 22 (submission open till Nov. 27 without penalty)

 

Reading 12: Thursday, Nov. 29

 

Lab 6 (TBA):

 

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Links to online resources

 

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