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
  • Informed
  • Heuristics
  • Local Search
  • 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 Presentation

  • First Order Predicate Logic
  • Prolog Programming
  • Unification
  • Forward Chaining
  • Backward Chaining
  • Resolution
  • Knowledge Representation
  • 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
  • Robot
  • Hardware
  • Perception
  • Planning
  • Moving

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


  • 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 pValue?
  • Chi-Square test
  • What is ANOVA?
  • Correlation vs Regression
  • Uses of Correlation & 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
  • Measure of Distribution (Skewness and Kurtosis), Box and Whisker Plot (Box Plot and its parts, Using Box Plots to compare distribution) and other statistical graphs


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

Time Series Analysis

  • Describe Time Series data
  • Format your Time Series data
  • List the different components of Time Series data
  • Discuss different kind of Time Series scenarios
  • 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
  • What is Time Series data?
  • 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.


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

Data Acquisition

  • Gather information from different sources.
  • Internal systems and External systems.
  • Web APIs, Open Data Sources, Data APIs, Web Scrapping
  • Relational Database access (queries) to process/access data

Data Pre-processing and Preparation

  • Data Munging, Wrangling
  • Plyr packages
  • Cast/Melt

Data Quality and Transformation

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

Handling Text Data

  • Bag-of-words
  • Regular Expressions
  • Sentence Splitting and Tokenization
  • Punctuations and Stop words, Incorrect spellings
  • 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 & Complexity
  • Challenges of processing Big Data (Volume, Velocity and Variety perspective)
  • Use Cases

Big Data Frameworks – Hadoop, Spark and NoSQL

  • Processing, Storage and Programming Framework
  • Hadoop eco-system 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

  • Science of Visualization
  • Visualization Periodic Table
  • Aesthetics and Story telling
  • 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 etc.

Sampling and Estimation

  • Sample versus population
  • Sample techniques (simple, stratified, clustered, random)
  • Sampling Distributions
  • Parameter Estimation
  • Unbalanced data treatment

Inferential Statistics

  • Develop an intuition how to understand the data, attributes, distributions
  • Procedure for statistical testing, etc.
  • 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

  • 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, multi-collinearity
  • Non-normality and Heteroscedasticity
  • Hypothesis testing of Regression Model
  • Confidence intervals of Slope
  • R-square and goodness of fit
  • Influential Observations – Leverage

Multiple Linear Regression

  • Polynomial Regression
  • Regularization methods
  • Lasso, Ridge and Elastic nets
  • Categorical Variables in Regression

Non-Linear Regression

  • Logit function and interpretation
  • Types of error measures (ROCR)
  • Logistic Regression in classification

Forecasting models

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


  • 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

Classification Naïve Bayes Classifier

  • Model Assumptions, Probability estimation
  • Required data processing
  • M-estimates, Feature selection: Mutual information
  • Classifier

K-Nearest Neighbors

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

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

Ensembles methods

  • Bagging & boosting and its impact on bias and variance
  • C5.0 boosting
  • Random forest
  • Gradient Boosting Machines and XGBoost

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

  • Image classification
  • Text classification
  • Image classification and hyper-parameter tuning
  • Emerging NN architectures

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 & 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 & 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

  • 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


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

  • Tensors
  • 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 decomposition–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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