tutorials/MOOCs

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ICWSM 2017 tutorial

http://www.icwsm.org/2017/program/tutorial/

T1: Building Your Own Online Lab with Volunteer Science

s T2: A Practical Introduction to Spatial Datasets and Urban Applications

s TA3: Polarization on Social Media

s TP4: Social Media for Health Research

AAAI 2017 tutorial

http://www.aaai.org/Conferences/AAAI/2017/aaai17tutorials.php

s Learn to Write a Scientific Paper of the Future: Reproducible Research, Open Science, and Digital Scholarship

Risk-Averse Decision Making and Control

Rulelog: Deep KRR for Cognitive Computing

IoT Big Data Stream Mining

Computer Poker

Recent Advances in Distributed Machine Learning

Statistical Relational Artificial Intelligence: Logic, Probability and Computation

AI Planning for Robotics

Modeling and Solving AI Problems in Picat

AI for Data-Driven Decisions in Water Management

Social Data Bias in Machine Learning: Impact, Evaluation, and Correction

Deep Learning Implementations and Frameworks

s Learning Bayesian Networks for Complex Relational Data

s Causal Inference and the Data-Fusion Problem

Eliciting High-Quality Information

Discrete Sampling and Integration for the AI Practitioner

Interactive Machine Learning: From Classifiers to Robotics

Knowledge Graph Construction from Text

Introduction to multiAgent Path Finding

Predicting Human Decision-Making: Tools of the Trade

Neuroevolution Reinforcement Learning

Artificial Intelligence and Video Games

ICDM 2017 tutorial

http://www.ucs.louisiana.edu/~sxk6389/Program/Tutorials.html

s Mining Misinformation in Social Media: Understanding Its Rampant Spread, Harm, and Intervention

s Challenges and Solutions in Group Recommender Systems

s Mining Cohorts & Patient Data: Challenges and Solutions for the Pre-Mining, the Mining and the Post-Mining Phases

KDD 2017 tutorial

http://www.kdd.org/kdd2017/tutorials

s Large Scale Hierarchical Classification: Foundations, Algorithms and Applications

s Mining Entity-Relation-Attribute Structures from Massive Text Data

s Deep Learning for Personalized Search and Recommender Systems

s A/B Testing at Scale: Accelerating Software Innovation

s Learning Representations of Large-scale Networks

Network Embedding- Enabling Network Analytics and Inference in Vector Space

s Smart Analytics for Big Time-series Data

s Safe Data Analytics: Theory, Algorithms, and Applications

s Recent Advances in Feature Selection: A Data Perspective

s Making Better Use of the Crowd

s Time Series data Mining Using the Matrix Profile: A Unifying View of Motif Discovery, Anomaly Detection, Segmentation, Classification, Clustering and Similarity Joins

s Data Mining in Unusual Domains with Information-rich Knowledge Graph Construction, Inference and Search

A Critical Review of Online Social Data: Biases, Methodological Pitfalls, and Ethical Boundaries

s Non-IID Learning in Big Data

s System Event Mining: Algorithms and Applications

s IoT in Practice: Case Studies in Data Analytics, with Issues in Privacy and Security

s Context-Rich Recommendation via Information Network Analysis Approach

s Urban Computing: Enabling Intelligent Cities with Big Data

s From Theory to Data Product: Applying Data Science Methods to Effect Business Change

s Data-Driven Approaches towards Malicious Behavior Modeling

s Machine Learning for Survival Analysis: Theory, Algorithms and Applications

s Athlytics: Data Mining and Machine Learning for Sports Analytics

NIPS 2017 tutorial

https://nips.cc/Conferences/2017/Schedule?type=Tutorial

Reinforcement Learning with People

A Primer on Optimal Transport

Deep Learning: Practice and Trends

Statistical Relational Artificial Intelligence: Logic, Probability and Computation

Fairness in Machine Learning

Deep Probabilistic Modelling with Gaussian Processes

Geometric Deep Learning on Graphs and Manifolds

Engineering and Reverse-Engineering Intelligence Using Probabilistic Programs, Program Induction, and Deep Learning

Differentially Private Machine Learning: Theory, Algorithms and Applications

CIKM 2017 tutorial

http://cikm2017.org/tutorialmain.html

Massively Scalable Production Grade Deep Learning with the Microsoft Cognitive Toolkit

Large Scale Distributed Data Science from Scratch with Apache Spark 2.0 & Deep Learning

s Knowledge Extraction and Inference from Text: Shallow, Deep, and Everything in Between

s Commonsense for Machine Intelligence: Text to Knowledge and Knowledge to Text

s Network Analysis in the Age of Large Network Dataset Collections - Challenges, Solutions and Applications

Task based Search: Understanding & Inferring User Tasks and Needs

s Construction and Querying of Large-scale Knowledge Bases

s Knowledge Graphs: In Theory and Practice

s Towards Space and Time Coupled Social Media Analysis

Malware Analysis for Data Scientists

WSDM 2017 tutorial

http://www.wsdm-conference.org/2017/tutorials/

s Neural Text Embeddings for Information Retrieval

s Utilizing Knowledge Graphs in Text-centric Information Retrieval

s Social Media Anomaly Detection: Challenges and Solutions

ECML/PKDD 2017 tutorial

http://ecmlpkdd2017.ijs.si/program.html#Tutorials

s Core Decomposition of Networks: Concepts, Algorithms, and Applications

s IoT Large Scale Learning from Data Streams

Interactive Adaptive Learning

s Machine learning with fossil data: analyzing environmental and climate change

s Deep Learning for Computer Vision Applications: Robotics and Driving

s AutoML - Automatic selection, configuration, and composition of machine learning algorithms

IJCAI 2017 tutorial

https://ijcai-17.org/tutorial-program.html

T1. Argumentation in Artificial Intelligence: From Theory to Practice

T2.IoT Big Data Stream Mining

T3. Interactive Machine Learning: From Classifiers to Robotics

T5. Acquisition, Representation and Usage of Conceptual Hierarchies

T6. Computational Models for Social Influence and Diffusion

T7. Energy-based machine learning

T8. Declarative Spatial Reasoning: Theory, Methods, and Applications

T9. Data Mining and Machine Learning using Constraint Programming Languages

T10. Markov Logic Networks: Recent Advances and Practical Applications

T11. Machine learning for dynamic social network analysis

T12. Learning and Decision-Making from Rank Data

T13. Theory and practice of revenue optimal mechanism design

T14. Multiwinner Elections: Applications, Axioms, and Algorithms

T15. Deep Reinforcement Learning

T16. Programming by Optimization: A Practical Paradigm for Computer-Aided Algorithm Design

T17. Multiagent Learning: Foundations and Recent Trends

T18. Unifying Logic, Dynamics and Probability: Foundations, Algorithms and Challenges

T19. Theoretical Analysis of Policy Iteration

T20. First-Order Multi-agent Logics in Action

T21. Heterogeneous Learning: Recent Advance and Future Studies

T22. Strategic Voting and AI

T23. Strategic Voting and Strategic Candidacy in Multi-Agent Systems

IEEE BigData 2017 tutorial

http://cci.drexel.edu/bigdata/bigdata2017/Tutorial.html

s Enterprise Knowledge Graphs for Large Scale Analytics

s Popularity on the Web: From Estimation to Prediction

s Security and Automated Platform Development for Big Data Analytics

s Time Series Data Mining using the Matrix Profile: A Unifying View of Motif Discovery, Anomaly Detection, Segmentation, Classification, Clustering and Similarity Joins

Mathematics of Big Data

s Industrial Big Data for Industrial Applications - Systematic Methodology

s Game Theory for Data Science: Eliciting truthful information

s Anti-discrimination Learning: From Association to Causation

s Building and Deploying Predictive Analytics Models Using the PMML Standard

Social Network Analysis with Python and NetworkX

https://pydata.org/barcelona2017/schedule/presentation/7/

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<demos>

Interactive demonstrations for ML courses

http://arogozhnikov.github.io/2016/04/28/demonstrations-for-ml-courses.html

demos (deep learning)