Teoretyczne Podstawy Informatyki

Introduction to Data Science (Wprowadzenie do analityki danych)

Wydział Fizyki, Astronomii i Informatyki Stosowanej,

Uniwersytet Jagielloński w Krakowie

Rok akademicki 2019/2020

Konsultacje: wtorek, godz. 13:00 - 14:00; pokój 2-D-11.

Egzam (theory part, written): 31.01.2020, 14.00-16.00, A-1-13
List of topics: link

Lectures & Assignments:
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Date	Lecture slides	Python scripts with assignments	Datasets	Tutorials
8.10.2019	Introduction Data exploration	assignement-0-python assignement-0-numpy assignement-0-numpy-matplotlib assignement-0-pandas assignment-1	kc_house_data.csv.zip info on kc_house	HowToStart DS_cheatsheet_numpy.pdf DS_cheatsheet_matplotlib.pdf DS_cheatsheet_jupyter_notebook.pdf DS_cheatsheet_pandas.pdf
15.10.2019	Regression-Primer Regression-Advanced-I	assignment-2		S.Rashki python-machine-learning-book rashka_ch04.ipynb rashka_ch10.ipynb
22.10.2019	Regression-Advanced-II	assignment-3		numpy tutorial matrix algebra tutorial
29.10.2019	Regression-Advanced-III Classification-Primer
5.11.2019	Classification-Advanced-I	assignment-4	data:amazon_baby.csv.zip	scikit-learn: LogisticRegression
12.11.2019	Classification-Advanced-II
19.11.2019	Classification-Advanced-III Clustering-Primer	assignment-5	data:people_wiki.cvs.zip
26.11.2019	Clustering&Retrieval-Advanced-I
3.12.2019	Clustering&Retrieval-Advanced-II	Its time for your personal Data Science project! Select one from the list below:
		Proj_NYcrime.txt Proj_PolandClimateChange.txt Proj_TrollTweets.txt Proj_VoteCast.txt Proj_Classification.txt Proj_Clustering.txt Proj_Regression.txt	lending-club-data.csv.zip http://snap.stanford.ed/data/amazon/ http://mlr.cs.umass.edu/ml/datasets.html https//data.world/
10.12.2019	Clustering&Retrieval-Advanced-III
7.01.2020	Modeling, simulation, Monte Carlo methods	assignement-6
14.01.2020	Wykład odwołany	assignement-7
21.01.2020	Statistical Inference			OpenIntro Statistics Z. Fan lectures at Standford Uni.
28.01.2020	Artificial Neural Network Recommending-System-Primer

Lectures are based on the materials from Coursera:

Dr. Mine Çetinkaya-Rundel "Data Analysis and Statistical Inference"
C. Guestrin and E. Fox, "Machine Learning Specialisation"
        Foundation: link
        Regression: link
        Classification: link
        Clustering and Retrieval: link

Related interesting material from Coursera
D. Peng, J. Leek and B. Caffo, " Exploratory Data Analysis"
J. Leskovec, A. Rajaraman and J. Ullman, "Mining Massive Datasets"
B. Caffo, R. D. Peng and J. Leek, "Regression Models"

Data Science applications in physics:
B. Nachman,
"Advanced Machine Learning for Classification, Regression, and Generation in Jet Physics"
M. Stoye,
"ML applications in CMS"
ML techniques in HEP, Workshop, Berkeley Laboratory, 11 - 13 December 2018
https://indico.physics.lbl.gov/indico/event/546/

Collection of datasets
http://mlr.cs.umass.edu/ml/datasets.html
http://faculty.marshall.usc.edu/gareth-james/ISL/data.html
http://snap.stanford.edu/data/amazon/

Useful links:
https://turi.com/download/install-graphlab-create-aws-coursera.html
https://turi.com/download/academic.html
https://github.com/turi-code/SFrame

Clustering
http://scikit-learn.org/stable/modules/generated/sklearn.feature_extraction.text.CountVectorizer.html
http://scikit-learn.org/stable/modules/generated/sklearn.feature_extraction.text.TfidfVectorizer.html

Boosting
https://turi.com/learn/userguide/supervised-learning/boosted_trees_classifier.html
https://homes.cs.washington.edu/~tqchen/pdf/BoostedTree.pdf
http://scikit-learn.org/stable/modules/generated/sklearn.ensemble.GradientBoostingClassifier.html
-----------------
[1] https://class.coursera.org/statistics
[2] http://www.openintro.org/stat/textbook.php
[3] https://class.coursera.org/exdata-006
[4] https://class.coursera.org/mmds
[5] http://www.mmds.org/
[6] http://www.cs.cmu.edu/~awm/tutorials.html

Additional materials
http://www.stat.cmu.edu/~cshalizi/ADAfaEPoV/ADAfaEPoV.pdf
http://statweb.stanford.edu/~tibs/ElemStatLearn/
http://en.wikipedia.org/wiki/Determining_the_number_of_clusters_in_a_data_set
http://www.youtube.com/watch?v=wQhVWUcXM0A

Link to lectures given in 2017
http://th-www.if.uj.edu.pl/~erichter/dydaktyka/Dydaktyka2017/AiSAD-2017/index.html

Link to lectures given in 2014
http://th-www.if.uj.edu.pl/~erichter/dydaktyka/Dydaktyka2014/AiSAD-2014/index.html

Ostatnia modyfikacja: 1 October 2019

Elzbieta Richter-Was

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