ARTIFICIAL INTELLIGENCE FELLOWSHIP PROGRAM
A rigorous 8 week post-doctoral training fellowship into Artificial Intelligence and Machine Learning.
Who is eligible?
Admission onto the AI Fellowship program is both selective and competitive. Only STEM PhD students from selected Universities are eligible.
This Fellowship program intends to bridge the gap between the scientific and engineering skills you will have acquired over the course of your degrees, and the skill base that employers are demanding.
Candidates who successfully complete the AI Fellowship program can expect to successfully secure employment as Data Scientists or Deep Learning Engineers. Due to the market demand far outstripping supply of deep learning skills, salaries are extremely competitive at this time and their value in the market is only expected to increase over the coming years.
Lectures, Labs, Guest Speakers
Experfy Harvard i-lab Certification
Job Placement or join our Startup Incubator
Peter Morgan is a published author and computer science industry veteran. Before entering industry, he solved high energy physics problems while enrolled in the PhD program in physics at the University of Massachusetts at Amherst. Peter then spent several years in industry as a Solutions Architect, and three years as a Research Associate on an experiment lead by Stanford University to measure the mass of the neutrino. He is currently Chief AI Officer at Ivy Data Science where he oversees AI technology strategy and platform & training development.
Dr. Zacharias Voulgaris has a Masters in Information Systems & Technology (City University of London), and a PhD in Machine Learning (University of London). He has worked at Georgia Tech as a Research Fellow and as a Data Scientist in both Elavon and G2. Zacharias has authored two books, Data Scientist: The Definitive Guide to Becoming a Data Scientist (2014), and Julia for Data Science (2016).
Week 1 – Fundamentals
Deep Learning in a Nutshell
SciPy, NumPy, Pandas
Databases – SQL/NoSQL
Frequentist vs Bayesian
Week 2 – Machine Learning
Naïve Bayesian Classifier
Support Vector Machines
Week 3 – Convolutional neural networks I
Computer Vision Overview
Convolutional neural networks
Week 4 – Convolutional neural networks II
Second order methods
Week 5 – Recurrent neural networks I
Recurrent neural networks
Natural language processing
Time series data
Week 6 – Recurrent neural networks II
Spark and Flink
Deep Learning Analysis
Week 7 – Reinforcement Learning
Week 8 – Latest Developments
Connection with physical law