ARTIFICIAL INTELLIGENCE FELLOWSHIP PROGRAM

A rigorous 8 week post-doctoral training fellowship into Artificial Intelligence and Machine Learning.

NYC

May 24th

San Francisco

May 24th

Boston

May 24th

NYC

July 31st

Who is eligible?

Admission onto the AI Fellowship program is both selective and competitive. Only STEM PhD students from selected Universities are eligible.

Course Details

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

Instructors

Peter Morgan

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.

Zacharias Voulgaris

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).

Curriculum

Week 1 – Fundamentals

Deep Learning in a Nutshell
Python
SciPy, NumPy, Pandas
Scikit-learn
Julia
Cloud computing
Databases – SQL/NoSQL
Statistics:
Frequentist vs Bayesian
Algorithms

Week 2 – Machine Learning

Supervised Learning:
Decision Trees
Naïve Bayesian Classifier
k-Nearest Neighbor
Support Vector Machines
Unsupervised Learning:
Clustering
PCA
TensorFlow Labs

Week 3 – Convolutional neural networks I

Computer Vision Overview
GPU computing
Convolutional neural networks
Frameworks
TensorFlow
Image classification
Autonomous vehicles
TensorFlow Labs

Week 4 – Convolutional neural networks II

Second order methods
Visualizing CNNs
VGGNet examples
Use cases
TensorFlow Labs

Week 5 – Recurrent neural networks I

Recurrent neural networks
LSTM
Natural language processing
Time series data
Image captioning
Video processing
TensorFlow Labs

Week 6 – Recurrent neural networks II

IoT Overview
Sensor data, Hardware
Streaming data
Stream processing
Spark and Flink
Deep Learning Analysis
TensorFlow Labs

Week 7 – Reinforcement Learning

Reinforcement learning
Deep Q Networks
Gaussian Processes
TensorFlow Labs

Week 8 – Latest Developments

Bayesian Inference
Neural Turing Machine
Biocomputing
GAN
WaveNet
Connection with physical law
Neuromorphic computing
Business Reporting
Wrap up

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