The main issue that will be addressed in this talk is predicting traffic jams and in order to minimize the time taken to reach a specific point. The talk itself will be humorous and will address a funny problem: "I hate traffic jams! I want to avoid them at all costs!".

The talk will be divided to chapters:

Chapter 1: Introduction - Problem presentation.

Topics:

 - The problem 
 - Big Data in general
 - Machine learning in general

Chapter 2: The data-set.

Topics:

 - Simple traffic data-set will be presented 
 - Data mining
 - Data Quality 
 - Visualizing Datasets
 - Classification VS Regression

Chapter 3: Basic Machine Learning.

Topics:

 - Linear Regression (Demo)
 - Polynomial Regression (Demo)
 - Overfitting 
 - Cross Validation (Demo)
 - Supervised Learning

Chapter 4: Feature Engineering.

Topics:     
-Features VS Raw Data
- Normalization
- Tokenization, Serialization

Chapter 5: Classification Basics.

Topics:

-From Regression to classification 
-Logistic Regression (Demo)

Chapter 6: Feature Learning.

Topics:     
-K-Means
- PCA
- LLE
- ICA

Chapter 7: SVMs

Topics:

-Support Vector Machines (Demo)
-Kernel Based Methods (Demo)

Chapter 8: Hyper parameter Optimization

Topics:

-Grid Search (Demo)
-Random Search
-Gradient Based Optimization

Chapter 9: KNN

Topics:

-K – Nearest Neighbors (Demo)

Chapter10: Random Forests:

Topics:

-Decision Trees (Demo)
-Random Forests (Demo)
-Entropy

Chapter 11: Ensemble learning.

Topics:

        - Bagging
        - Boosting 
        - Bayesian parameter averaging

- Final Live Demo: Beating the traffic jam with an incredibly over-killing algorithm.