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 – 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 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

---

« CodesCracker Home Python Tutorial »

---