Introduction to Data Science and Machine Learning

Enrollment in this course is by invitation only

Course in Machine learning which will cover topics like Mathematics of Machine Learning, Applicability of Machine Learning, Supervised Machine Learning, Unsupervised Machine Learning and Introduction to Neural Networks. This course has been specifically designed for programmers and system developers who have good experience with programming.

About This Course

Machine learning is the science of teaching machines how to act independently by training them using historical data. With the availability of cheap computing power and digitization of data we are witnessing a paradigm shift where computer is taking a pivotal place in human life. A well programmed computer can now handle lot of seemingly complex tasks like driving cars, web search, speech recognition, sentiment analysis, sorting mails, robotics and many other applications. In this course you will learn techniques which can be used for training computers to produce reliable future forecasts based on historical data.

Course Contents

1. Introduction to Machine Learning: What & How

A. Definitions and Basic Concepts: data, algorithms & the 3 components(REO)

B. Types of ML: Supervised, Unsupervised & Reinforcement Learning

C. How does ML work? Features, models and ML workflow

2. On the Applicability of ML: Why & When

A. Extracting Intelligence from data

B. The “Problem space” of Machine Learning

C. Bias vs. Variance problem

D. Intro to Scikit-learn Py

3. Short intro to Linear Algebra

A. Vectors, matrices and models (1)

B. Vector operations, change of basis, linear independence (2)

C. Matrices and transformations (3, 4)

D. Eigenvalues and Eigenvectors, SVD (5)

4. Short intro to Multivariate Calculus

A. Differentiations and the Chain rule (1, 2, 3)

B. Power series; linearization and approximations (4)

C. The Gradient Descent and its use in Optimization (5)

D. Objective functions and the minimization of errors (6)

5. Supervised ML — Regression problems

A. Linear Regression

B. Polynomial Regression

C. Other types of regression: Ridge, Lasso, ElasticNet

6. Supervised ML — Classification problems — Logistic Regression

A. Logistic Regression

B. Regularization

C. ROC curve

7. Supervised ML — Artificial Neural Networks — the Perceptron

A. Perceptron concept

B. Py implementation lab

C. Applications

8. Supervised ML — Classification problems - SVM

A. Introduction

B. Py implementation lab

C. Applications

9. Unsupervised ML

A. Clustering problems: K-Means

B. Dimensionality Reduction: PCA

C. Py implementation lab

D. Applications

10. Deep Neural Networks

A. Convolutional Neural Networks

B. Py implementation lab

C. Applications

Course Objectives

This course is designed for programmers and system developers. This course will achieve following objectives:

Get them introduced to data science and machine learning
Provide mathematical foundation to get good understanding of advanced topics
Inspire them to develop models which can solve business problems more efficiently

Requirements

Comfortable with Python Programming
Strong Background in Statistics and Probability (mean, variance, correlation, regression etc)
some understanding of Matrix Algebra (Matrix Multiplication, Matrix Inversion)

Length: 10 Week
Effort Required: 90 minutes lecture + 5 hours per week for Projects and Assignments
Live Lectures: Every Week
Coding environment: Py Jupyter Notebook

Course Staff

Cris Doloc

Cris Doloc is the founder and principal of FintelligeX. Cris holds a PhD in Computational Physics and have more than 25 years of experience as a quant, computational expert and algo trading system architect. He currently teaches at the University of Chicago in the programs of Financial Mathematics and Biomedical Informatics.

"The most important lesson that I have learned in my career, is that the potential to solve complex business problems depends mostly on being able to recruit, train and develop the necessary talent that could apply efficiently the latest technology available." Cris Doloc