Artificial Intelligence

Artificial Intelligence is so popular that it’s no more just an option for businesses. Businesses have already started making the technology an essential part of their strategies.

This Master’s program is designed to teach the underlying concepts of Artificial Intelligence (AI) and machine learning and how they can be used to solve real-world problems.

Students will gain a deep knowledge of the math of machine learning, including relevant tools and languages and popular algorithms and their applications.

They’ll also gain a better understanding of the principles and practices of artificial intelligence (AI) and how to put them to work, from the basics Machine Learning, Natural language processing and Robotics.

Going beyond the theory, our approach invites participants to deep dive into the real scenarios, where learning is enriched by specialists in the fields of artificial intelligence.

We expect learners would be required to put in 12-16 hours per week.

Structure

Module 1: Occidental Identity

Occidental Identity
Ancient and Classic Heritage
Relations between State and Religion

Module 2. Subject 1

1. INTRODUCTION TO ARTIFICIAL INTELLIGENCE

What is Artificial Intelligence?
The foundation & History of Al
Machine Learning
Natural Language Processing
Robotics

2. AGENTS & ENVIRONMENTS

What is an agent?
Rationality
Structure of Intelligent Agents

3. SEARCH ALGORITHMS

What is Searching in AI?
Search Terminology
Types of Search

4. FUZZY LOGIC SYSTEMS

What is Fuzzy Logic?
Why Fuzzy Logic?
Sample Applications

5. ETHICAL AI

Module 2. Subject 2

PYTHON TO MACHINE LEARNING – INTRODUCTION

1. RAW DATA PROCESSING

Creating Arrays
Data Processing
Data Cleansing
Data Operations

2. INFERENTIAL STATISTICS

Normal
Distribution
Poisson
Distribution
z-score
p-value

3. DATA MINING

Presenting an analysis
Example code

4. ADVANCED VISUALIZATION

Different types of Plots
Box Plots
Heatmaps
Scatter plots etc.

Module 2. Subject 3

1. APPLICATIONS OF AI

2. FORMS OF LEARNING

Forms of Learning
Supervised Learning
Reinforcement learning

3. SUPERVISED & UNSUPERVISED LEARNING

3.1 Linear Models – Regression & Classification

Maximum Likelihood
Least Squares
Regularization

3.2 Bayesian Methods

Bayes Rule
MAP Inference
Active Larning

3.3 Foundational Classification Algorithms

Nearest Neighbors
Perceptron
Logistic Regression

3.4 Intermediate Classification Algorithms

SVM
Decision Trees
Random Forests and
Gradient (XG) Boosting

4. PRACTICAL ON SUPERVISED LEARNING

1. MACHINE LEARNING WITH PYTHON

Decision Trees
Linear Regression
Logistic Regression

2. KMEANS CLUSTERING

3. ESTIMATING LIKELIHOOD OF AN EVENT

Data Preparation
Create Training and Test Datasets
Build a model
Evaluate a model
Build and Evaluate using Scikit

4. UNSTRUCTURED DATA ANALYSIS WITH TEXT MINING

Data Preprocessing
Creating a word cloud
Stemming and Lemmatization

Module 3. Subject 5

1. UNSUPERVISED LEARNING

1.1 Clustering Methods

K-Means
Clustering
EM
Gaussian Mixtures

1.2 Recommendation

Systems
Collaborative
Filtering
Topic
Modeling
PCA

2. NATURAL LANGUAGE PROCESSING

Language Models
Text Classification

3. IMAGE PROCESSING

Language Models
Text Classification

4. REINFORCEMENT LEARNING

4.1 Reinforcement Learning

Reinforcement Learning Introduction
Examples
Elements of Reinforcement Learning
Limitations and Scope

4.2 Tabular Solution Methods

Multi-arm Bandits
Gradient Bandits
Associative Search (Contextual Bandits)

4.3 Finite Markov Decision Process (MDP)

The Agent-Environment Interface
Goals and Rewards
Returns
The Markov Property
Markov Decision Processes
Value Functions
Optimal Value Functions
Optimality and Approximation

4.4 Monte Carlo Methods

Monte Carlo Prediction
Monte Carlo Estimation of Action Values
Monte Carlo Control
Monte Carlo Control without Exploring Starts

Module 4

SAMPLE ASSIGNEMENTS (will change in future)