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# Artificial Intelligence (AI) Course Syllabus

This article is created and published to provide the complete subject-wise syllabus of
**Artificial Intelligence**, or in short AI.

## Foundations of Artificial Intelligence

This section covers the syllabus on the foundations of Artificial Intelligence (AI).

### Introduction to Artificial Intelligence

- Artificial Intelligence Introduction
- Future of Artificial Intelligence
- Characteristics of Intelligent Agents
- Typical Intelligent Agents

### Problem Solving Methods

- Problem solving Methods
- Search Strategies
- Uninformed and Informed Search
- Local Search
- Heuristics
- Algorithms and Optimization Problems
- Searching with Partial Observations
- Constraint: Satisfaction Problems, Constraint Propagation, Backtracking Search
- Game Playing
- Optimal Decisions in Games
- Alpha-Beta Pruning
- Stochastic Games

### Knowledge Representation

- Knowledge Representation
- First-Order Predicate Logic
- Prolog Programming
- Unification
- Forward and Backward Chaining
- Resolution
- Ontological Engineering
- Categories and Objects
- Events
- Mental Events and Mental Objects
- Reasoning Systems for Categories
- Reasoning with Default Information

### Software Agents

- Architecture for Intelligent Agents
- Agent communication
- Negotiation and Bargaining
- Argumentation among Agents
- Trust and Reputation in Multi-agent systems

### Artificial Intelligence Applications

- Artificial Intelligence applications
- Language Models
- Information Retrieval
- Information Extraction
- Natural Language Processing
- Machine Translation
- Speech Recognition
- Robotics
- Hardware and Software for Robots
- Planning and Perception

### R Programming Essentials

- Syntax
- Commands
- Packages
- Libraries
- Data Types
- Data Structures:
- Vectors
- Matrices
- Arrays
- Lists
- Factors
- Data Frames

- Importing and Exporting Data
- Control structures
- Functions

### Python Programming Essentials

- Python Overview
- About Interpreted Languages
- Advantages/Disadvantages of Python pydoc.
- Starting Python
- Interpreter PATH
- Using the Interpreter
- Running a Python Script
- Using Variables
- Keywords
- Built-in Functions
- Strings Different Literals
- Math Operators and Expressions
- Writing to the Screen
- String Formatting
- Command Line Parameters and Flow Control
- Lists
- Tuples
- Indexing and Slicing
- Iterating through a Sequence
- Functions for all Sequences
- Operators and Keywords for Sequences
- List Comprehensions
- Generator Expressions
- Dictionaries and Sets
- Numpy and Pandas
- Learning NumPy
- Introduction to Pandas
- Creating Data Frames
- Grouping/Sorting
- Plotting Data
- Creating Functions
- Slicing/Dicing Operations

### Statistics

- What is Statistics?
- Descriptive Statistics
- Central Tendency Measures
- The Story of Average
- Dispersion Measures
- Data Distributions
- Central Limit Theorem
- What is Sampling?
- Why Sampling?
- Sampling Methods
- Inferential Statistics
- What is Hypothesis testing?
- Confidence Level
- Degrees of freedom
- What is p-value?
- Chi-Square test
- What is ANOVA?
- Correlation vs Regression
- Uses of Correlation and Regression

### Descriptive Statistics

- Data exploration:
- Histograms
- Bar chart
- Box plot
- Line graph
- Scatter plot

- Qualitative and Quantitative Data
- Measure of Central Tendency:
- Mean
- Median
- Mode

- Measure of Positions:
- Quartiles
- Deciles
- Percentiles
- Quantiles

- Measure of Dispersion:
- Range
- Median
- Absolute deviation about median
- Variance and Standard deviation

- Anscombe's quartet
- Other Measures:
- Quartile and Percentile
- Interquartile Range

### Statistical Analysis

- Relationship between attributes: Covariance, Correlation Coefficient, Chi-Square
- Skewness and Kurtosis
- Box and Whisker Plot

### Probability

- Probability (Joint, marginal and conditional probabilities)
- Probability distributions (Continuous and Discrete)
- Density Functions and Cumulative functions

### Time Series Analysis

- Time Series Analysis
- Describe Time Series data
- Format your Time Series data
- Different Types of Components of Time Series data
- Discuss different kinds of Time Series scenario
- Choose the model according to the Time series scenario
- Implement the model for forecasting
- Explain working and implementation of ARIMA model
- Illustrate the working and implementation of different ETS models
- Forecast the data using the respective model
- Time Series variables
- Different components of Time Series data
- Visualize the data to identify Time Series Components
- Implement ARIMA model for forecasting
- Exponential smoothing models
- Identifying different time series scenario based on which different Exponential Smoothing model can be applied
- Implement respective model for forecasting
- Visualizing and formatting Time Series data
- Plotting decomposed Time Series data plot
- Applying ARIMA and ETS model for Time Series forecasting
- Forecasting for given Time period

## Data Management

This section covers the syllabus on data management used in AI.

### Data

- Basis of Data Categorization
- Types of Data
- Data Collection Types
- Forms of Data and Sources
- What is Data Architecture?
- Components of Data Architecture
- OLTP vs OLAP
- How is Data Stored?

### Data Acquisition

- Gather information from different sources
- Internal and External systems
- Web APIs, Open Data Sources, Data APIs, Web Scrapping
- Relational Database access

### Data Preprocessing and Preparation

- Data Preprocessing
- Data Preparation
- Data Munging/Wrangling
- dplyr package
- Casting/Melting

### Data Quality and Transformation

- Data Quality and Changes
- Data Quality Issues
- Data Quality Story
- Data imputation
- Data Transformation (minmax, log transform, z-score transform etc.)
- Binning, Classing and Standardization
- Outlier/Noise and Anomalies

### Handling Text Data

- Bag-of-words
- Regular Expressions
- Sentence Splitting and Tokenization
- Punctuation and Stopwords in correct spelling
- Properties of words and Word cloud
- Lemmatization and Term-Document TxD computation
- Sentiment Analysis (Case Study)

### Big Data

- What is Big Data?
- Big Data Architecture
- Big Data Technologies
- Big Data Challenge
- Big Data Requirements
- Big Data Distributed Computing and Complexity
- Challenges of processing Big Data (Volume, Velocity and Variety perspective)
- Use Cases

### Big Data Frameworks â€“ Hadoop, Spark and NoSQL

- Big Data Frameworks (Hadoop, Spark and NoSQL)
- Processing, Storage and Programming Framework
- Hadoop ecosystem Components and their functions
- Essential Algorithms (Word count, Page Rank, IT-IDF)
- Spark: RDDs, Streaming and Spark ML
- NoSQL concepts (CAP, ACID, NoSQL types)

## Statistical Decision Making

This section covers the syllabus on statistical decision making used in AI.

### Data Visualization

- Introduction to Data Visualization and its Importance
- Science of Visualization
- Visualization Periodic Table
- Aesthetics and Storytelling
- Concepts of measurement - scales of measurement
- Design of data collection formats with illustration
- Principles of data visualization - different methods of presenting data in business analytics
- Concepts of Size, Shape, Color
- Various Visualization types
- Bubble charts
- Geo-maps (Chlorpeths)
- Gauge charts
- Tree map
- Heat map
- Motion charts
- Force Directed Charts

### Sampling and Estimation

- Sampling and Estimation
- Sample versus population
- Sample techniques (simple, stratified, cluster, random)
- Sampling Distributions
- Parameter Estimation
- Unbalanced data treatment

### Inferential Statistics

- Develop an intuition on how to understand the data, attribute, and distributions
- Procedure for statistical testing
- Test of Hypothesis (Concept of Hypothesis testing, Null Hypothesis and Alternative Hypothesis)
- Cross Tabulations (Contingency table and their use, Chi-Square test, Fisher's exact test)
- One Sample t test (Concept, Assumptions, Hypothesis, Verification of assumptions, Performing the test and interpretation of results)
- Independent Samples t test
- Paired Samples t test
- One way ANOVA (Post hoc tests: Fisher's LSD, Tukey's HSD)
- Z-test and F-test

## Predictive Analytics

This section covers the syllabus on predictive analysis used in AI.

### Linear Regression

- Linear regression Definition and Description
- Regression basics: Relationship between attributes using Covariance and Correlation
- Relationship between multiple variables: Regression (Linear, Multivariate) in prediction
- Residual Analysis
- Identifying significant features, feature reduction using AIC, multicollinearity
- Non-normality and Heteroskedasticity
- Hypothesis testing of Regression Model
- Confidence interval of Slope
- R-squared and goodness of fit
- Influential Observations - Leverage

### Multiple Linear Regression

- Multiple Linear Regression Definition and Description
- Polynomial Regression
- Regularization methods
- Lasso, Ridge and Elastic net
- Categorical Variables in Regression

### Nonlinear Regression

- Nonlinear Regression definition and description
- Logit function and interpretation
- Types of error measures (ROCR)
- Logistic Regression in classification

### Forecasting models

- Forecasting models
- Trend analysis
- Cyclical and Seasonal analysis
- Smoothing, Moving averages, Box-Jenkins, Holt-winters, Auto-correlation, ARIMA
- Applications of Time Series in financial markets

### Clustering

- Clustering
- Distance measures
- Different clustering methods (Distance, Density, Hierarchical)
- Iterative distance-based clustering
- Dealing with continuous, categorical values in K-Means
- Constructing a hierarchical cluster
- K-Medoids, k-Mode and density-based clustering
- Measures of quality of clustering

### Naive Bayes Classifiers

- Naive Bayes Classifiers
- Model Assumptions, Probability estimation
- Required data processing
- M-estimates, Feature selection: Mutual information

### K-Nearest Neighbors

- K-Nearest Neighbors
- Computational geometry, Voronoi Diagrams, and Delaunay Triangulations
- K-Nearest Neighbor algorithm
- Wilson editing and triangulations
- Aspects to consider while designing K-Nearest Neighbor

### Support Vector Machines

- Support Vector Machines
- Linear learning machines and Kernel space, Making Kernels and working in feature space
- SVM for classification and regression problems

### Decision Trees

- What is Decision Tree?
- How to build Decision trees?
- Creating a Decision Tree
- What is Classification and its use cases?
- Algorithm for Decision Tree Induction
- Confusion Matrix
- ID4
- C4.5
- CART

### Ensemble Methods

- Ensemble methods
- Bagging and boosting and its impact on bias and variance
- C5.0 boosting
- Random forest
- Gradient Boosting Machines and XGBoost

### Association Rule Mining

- Association Rule Mining
- The applications of Association Rule Mining: Market Basket, Recommendation Engines, etc.
- A mathematical model for association analysis; Large item sets; Association Rules
- Apriori: Constructs large item sets with mini sup by iterations; Interestingness of discovered association rules
- Application examples; Association analysis vs. classification
- FP-trees

## Artificial Intelligence, Data Science, Deep Learning, Machine Learning

This section covers the syllabus of all three major topics of Artificial Intelligence including AI itself.

### Foundations for Artificial Intelligence

- Artificial Intelligence: Application areas
- Artificial Intelligence Basics (Divide and Conquer, Greedy, Branch and Bound, Gradient Descent)
- NN basics (Perceptron and MLP, FFN, Backpropagation)
- Scientific Method
- Modeling Concepts
- CRISP-DM Method

### Convolution Neural Networks

- Convolution Neural Networks
- Image classification
- Text classification
- Image classification and hyper-parameter tuning
- Emerging NN architectures

### Recurrent Neural Networks

- Recurrent Neural Networks
- Building recurrent NN
- Long Short-Term Memory
- Time Series Forecasting

### Data Science Deep Dive

- What Data Science is?
- Why Data Scientists are in demand?
- What is a Data Product?
- The growing need for Data Science
- Large Scale Analysis Cost vs Storage
- Data Science Skills
- Data Science Use Cases
- Data Science Project Life Cycle and Stages
- Data Acuqisition
- Where to source data?
- Techniques
- Evaluating input data
- Data formats
- Data Quantity
- Data Quality
- Resolution Techniques
- Data Transformation
- File format Conversions
- Annonymization
- Functions
- Function Parameters
- Global Variables
- Variable Scope and Returning Values. Sorting
- Alternate Keys
- Lambda Functions
- Sorting Collections of Collections
- Classes and OOPs

### Deep Learning

- What is Deep Learning?
- Need for Data Scientists
- What is Business Intelligence
- What is Data Analysis
- What is Data Mining
- Advantage of Deep Learning over Machine learning
- Reasons for Deep Learning
- Real-Life use cases of Deep Learning
- Auto-encoders and unsupervised learning
- Stacked auto-encoders and semi-supervised learning
- Regularization - Dropout and Batch normalization

### Machine Learning

- Machine Learning (ML)
- ML Techniques overview
- Value Chain
- Types of Analytics
- Principal components analysis (Eigen values, Eigen vectors, Orthogonality)
- Lifecycle Probability
- Analytics Project Lifecycle
- Validation Techniques (Cross-Validations)
- Feature Reduction/Dimensionality reduction
- Review of Machine Learning

## Tensorflow

This section covers the syllabus of tensorflow with multiple technology.

### Tensorflow with Python

- Introducing Tensorflow
- Introducing Tensorflow
- Why Tensorflow?
- What is tensorflow?
- Tensorflow as an Interface
- Tensorflow as an environment
- Tensors
- Computation Graph
- Installing Tensorflow
- Tensorflow training
- Prepare Data
- Tensor types
- Loss and Optimization
- Running tensorflow programs

### Building Neural Networks using Tensorflow

- Building Neural Networks using Tensorflow
- Tensorflow data types
- CPU vs GPU vs TPU
- Tensorflow methods
- Introduction to Neural Networks
- Neural Network Architecture
- Linear Regression example revisited
- The Neuron
- Neural Network Layers
- The MNIST Dataset
- Coding MNIST NN

### Deep Learning using Tensorflow

- Deepening the network
- Images and Pixels
- How humans recognise images
- Convolutional Neural Networks
- ConvNet Architecture
- Overfitting and Regularization
- Max Pooling and ReLU activations
- Dropout
- Strides and Zero Padding
- Coding Deep ConvNets demo
- Debugging Neural Networks
- Visualising NN using Tensorflow
- Tensorboard

### Transfer Learning using Keras and TFLearn

- Transfer Learning Introduction
- Google Inception Model
- Retraining Google Inception with our own data demo
- Predicting new images
- Transfer Learning Summary
- Extending Tensorflow
- Keras
- TFLearn
- Keras vs TFLearn Comparison

## Case Studies

This is the last section, that covers some cases studies of Artificial Intelligence (AI).

### Churn Analysis and Prediction (Survival Modelling)

- Cox-proportional models
- Churn Prediction

### Credit card Fraud Analysis

- Imbalanced Data
- Neural Network

### Sentiment Analysis or Topic Mining from New York Times

- Part-of-Speech Tagging
- Stemming and Chunking

### Sales Funnel Analysis

- A/B testing
- Campaign effectiveness, Web page layout effectiveness
- Scoring and Ranking

### Recommendation Systems and Collaborative filtering

- User based
- Item Based
- Singular value decompositionâ€“based recommenders

### Customer Segmentation and Value

- Segmentation Strategies
- Lifetime Value

### Portfolio Risk Conformance

- Risk Profiling
- Portfolio Optimization

### Uber Alternative Routing

- Graph Construction
- Route Optimization

Artificial Intelligence Online Test

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