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Skills you will gain

- Python Programming
- Linear Regression
- Logistic Regression
- Artificial Intelligence
- Computer Programming
- Loops
- Functions
- Conditional Statements
- Machine Learning Concepts
- Machine Learning Algorithms

Programming Languages, Tools & Libraries Covered

About this Specialization

This specialization course is designed for those who want to gain hands-on experience in solving real-life problems using machine learning. You will learn how to implement and apply predictive, classification, clustering, and information retrieval machine learning algorithms to real datasets throughout the course.

In the Project, you’ll use the technologies learned throughout the Specialization to design and create your own applications for data retrieval, processing, and visualization.

How the Specialization Works

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**Hands-on Project**

Every Specialization includes a hands-on project. You'll need to successfully finish the project(s) to complete the Specialization and earn your certificate. If the Specialization includes a separate course for the hands-on project, you'll need to finish each of the other courses before you can start it.

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When you finish every course and complete the hands-on project, you'll earn a Certificate that you can share with prospective employers and your professional network.

Course Fee 30,000 + 18% GST

Enroll and Pay NowCourses in this Specialization

Course

1

Course 1

Python Programming – A Practical Approach

**Introduction**In this lesson, you will learn about the process of programming, which involves different steps. This will give you a brief introduction to programming.

**The Programming Cycle for Python**In this lesson, you will learn about the programming cycle of Python, which includes different steps. Then you will learn about the Python IDE that is to be used and the I Python shell.

**Interacting with Python Programs**In this lesson, you will learn to write simple codes or programs to read some value from the user or print some computed value. In other words, you will learn to interact with some easy Python programs.

**Elements of Python**In this lesson, you will learn about the elements of Python program or you can say, the parts of the Python Program. Then you will also learn about the Data types in Python

**Type Conversion**In this lesson, you will learn about Type Conversion or Type Casting. You will also learn to convert types or perform typecasting Python.

**Expressions**In this lesson, you will learn about the expressions, particularly different types of operators that are available in Python programming language. You will also learn about variables and Identifiers.

**Assignment Statement**In this lesson, you will learn about the assignment operators, assignment statements and comments in Python.

**Arithmetic Operators**In this lesson, you will learn about the Arithmetic Operators in Python, which are Binary Operators, Unary Operators and // Operator

**Operator Precedence**In this lesson, you will learn about the operator precedence in Python.

**Boolean Expression**In this lecture, you will learn about the Boolean Expressions, Relational Operators, Logical Operators and Complex Expressions in Python.

**Conditionals**In this lesson, you will learn about conditional statement in Python, which is popularly known as if-else statement, its working and execution. You will also learn about the importance of Indentation in writing conditional codes.

**Conditionals (Continued)**In this lecture, you will continue to learn about conditional statements. You will learn about nested-if statement and Elif statement in Python along with working example.

**Expression Evaluation**In this lecture, you will learn to evaluate the expressions in Python, the short circuit evaluation method, three factors for expression evaluation.

**Float Representation**In this lecture, you will learn about floating point representation in more detail along with the cautions about using floats and comparing these floats.

**Loops**In this lesson, you will learn about the purpose and working of loops and then you will learn about the While loop including its working.

**Loops (Continued)**In this lesson, you will continue to learn about some terminologies associated with Loops followed by the concept of Loop invariant and the common mistakes are committed by the programmers.

**For Loop**In this lesson, you will learn about the important function called as range() in Python, then you will learn about the For loop and its working with the help of an example.

**Nested Loops**In this lesson, you will learn about the Nested loops in Python. A Nested Loop is nothing but a loop within the loop. You will also understand the working of Nested loop with the help of examples.

**Break and Continue**In this lesson, you will learn about the Break and Continue keywords in Python. You will also learn about the working of Break and Continue statements with the help of examples.

**Function**In this lesson, you will learn about the Functions, why functions are needed and why functions are used.

**Parts of A Function**In this lesson, you will learn about the different parts of a function. Then you will learn about function call in Python Programming.

**Execution of A Function**In this lesson, you will learn about the execution of the function, and what a Stack is.

**Keyword and Default Arguments**In this lesson, you will learn about the Keyword Arguments and the Default Arguments in Python Programming.

**Scope Rules**In this lecture you will learn about the scope of names of the variables in Python Programming. You will also learn about the scope rules in case of variables, functions etc. and Global Variable.

**Strings**In this lesson, you will learn about the most important topic in Python Programming, which is Strings. Then you will also learn to find the length of the string and perform Concatenation and Repeat operations in it.

**Indexing and Slicing of Strings**In this lesson, you will understand the concept of Indexing and Slicing of the Strings. You will learn these with the help of examples.

**More Slicing**In this lesson, you will continue to look at Slicing in more detailed manner with the help of example.

**Tuples**In this lesson, you will learn about another kind of datatype in Python Programming which is known as Tuple.

**Unpacking Sequences**In this lesson, you will learn about unpacking the sequences in Python Programming. The examples of Sequences are Strings and Tuples.

**Lists**In this lesson, you will learn about one of the most useful datatype in Python Programming, known as List A List is nothing but an ordered sequence of values.

**Mutable Sequences**In this lesson, you will learn about the mutable and immutable types of sequences in Python Programming.

**List Comprehension**In this lesson, you will learn the concept of list Comprehension, which is a concise way to build the list in Python programming.

**Sets**In this lesson, you will learn about another kind of sequences in Python programming, which is known as Sets. Sets are nothing but an unordered collection of elements with no duplicate elements.

**Dictionaries**In this lesson, you will learn about the most used datatype in Python, which is known as Dictionary, which is an unordered set of Key:Value pairs.

**Higher Order Functions**In this lesson, you will learn about higher order functions. Python allows you to treat functions as first class Objects. Then you will also learn about the Lambda Expressions.

**Sieve of Eratosthenes**In this lesson, you will look at the program to generate prime numbers with the help of an algorithm given by the Greek Mathematician named Eratosthenes, whose algorithm is known as Sieve of Eratosthenes.

**File I/O**In this lesson, you will learn about the file input and output operations in Python Programming. Files in computer represent persistent storage, which means that any data you write to the file remain available even after the program exists.

**Exceptions**In this lesson, you will learn about the Exceptions in Python Programming. Exceptions are the way of Python to tell the user that something unexpected has happened.

**Assertions**In this lesson, you will learn about Assertions in Python. An assertion is a way of validating an assumption or a condition.

**Modules**In this lesson, you will learn about the modules in Python. Python allows you to keep the definitions in a file and use it in a script or an interactive instance of the interpreter. This file is known as the module.

**Importing Modules**In this lesson, you will learn to import some specific functions from a module and the use of _main_ in Modules in Python Programming.

**Abstract Data Types**In this lesson, you will learn about the abstract data types and ADT interface in Python Programming.

**Classes**In this lesson, you will learn about the most important concept of Python programming, which is Classes. You will learn about class definition and other operations in the classes.

**Special Methods**In this lesson, you will learn about some special methods in Python programming such as _init_, _str_, comparison methods and Arithmetic methods etc.

**Class Example**In this lesson, you will understand the concept of classes in Python with the help of an example.

**Inheritance**In this lesson, you will learn about the important Object Oriented Programming Concept, which is known as Inheritance.

**Inheritance and OOP**In this lesson, you will understand the concept of Inheritance in Python Programming, in more detailed manner by taking an example.

**Iterators**In this lesson, you will learn about the most important statement in Python Programming, which is considered as the backbone of for...in statements. These are known as Iterators.

**Recursion**In this lesson, you will learn about Recursion, which is an important concept in Python Programming. When a function calls itself, this is termed as Recursion. You will also learn the properties of a Recursive Function.

**Simple Search**In this lesson, you will learn to perform a simple search in a given sequence in Python Programming.

**Estimating Search Time**In this lesson, you will learn to estimate the time taken by the program in Python Programming.

**Binary Search**In this lesson, you will learn to perform faster searching in the sequence with the help of Binary Search in Python Programming.

**Estimating Binary Search Time**In this lesson, you will learn to estimate the time taken by the Binary Search program in Python Programming.

**Recursive Fibonacci**In this lesson, you will understand the difference between recursion and iteration followed by implementing the recursive function to generate the Fibonacci series in Python Programming.

**Tower Of Hanoi**In this lesson, you will learn about the Tower of Hanoi problem and its solution by implementing the Recursive function in Python Programming.

**Sorting**In this lesson, you will learn about an important concept in Python Programming, which is known as Sorting.

**Selection Sort**In this lesson, you will learn about one of the sorting technique or a sorting algorithm in Python Programming popularly known as Selection Sort.

**Merge List**In this lesson, you will learn about merging the two lists which are sorted in Python programming.

**Merge Sort**In this lesson, you will understand the working and implementation of another popular sorting algorithm in Python programming known as Merge Sort.

**Higher Order Sort**In this lesson, you will learn about Higher Order Functions and how to use these higher order functions to make a generic sorting function. Then you will also learn about Generic Selection Sort Function.

**Python Programming - Final Quiz**This is the final part in this course and contains a set of questions for your self evaluation. The course will not be considered as completed successfully, if you ignore this quiz.

Course

2

Course 2

Introduction to Machine Learning

**Supervised Learning:**

- Linear regression
- Maximum likelihood estimation
- Regularization/Maximum a posteriori estimation

- Logistic regression/ Classification
- Gradient Descent
- Multiclass classification

- Support Vector Machine
- Duality
- Hard/Soft margin SVM

**Unsupervised Learning:
**

- Clustering
- K-means Hard / Soft
- Expectation Maximization

- Principal Component Analysis
- Singular value decomposition

**Non-linear methods:
**

- Decision trees, Nearest Neighbours (on transformed features)
- Neural networks
- Backpropagation
- Dropout
- CNN, RNN

- Kernel learning
- regression, SVM, k-means, k-NN

**Ensemble methods:
**

- Boosting and Bagging
- Adaboost, Random Forest, Gradient boosting

Course

3

Course 3

Machine Learning

**Introduction**- What is the history of Machine Learning?
- What is the difference between Machine Learning solution and programmatic solution?
- What is a formal definition of Machine Learning?
- What are some domains and examples of Machine Learning?
- How can we create a (machine) learner?

**Different types of Machine Learning**- What are the broad types of Machine Learning?
- What is Unsupervised / Supervised / Semisupervised and Reinforcement learning?
- What is supervised learning? (In detail)
- What are some examples of Classification and Regression problems?
- What are Features, Some of the Sample training examples of feature and Can we draw some Schematic Diagrams (for Supervised learning)?
- What is Classification Learning? and what are some of its tasks and performance metric?
- How do we get data for the learning problems? How are representations of functions used in Machine Learning? What is the hypothesis space?

**Hypothesis Space and Inductive Bias**- What is inductive learning?
- What are the features and feature vectors?
- What is the start of the classification problem? What are feature space and hypothesis space for Classification problems?
- 5 types of representations of a function
- Hypothesis space
- Terminology (example, training data, instance space, concept, target function)
- What is the size of the hypothesis space (for n boolean features) and what is hypothesis language?
- What is inductive learning hypothesis?
- What are inductive learning and consistent hypothesis? Why is inductive learning an ill-posed problem?
- What are various types of bias? (Occam's Razor, MDL, MM) and what are the important issues in Machine Learning? What is generalization? (Bias and Variance)

**Evaluation and Cross-Validation**- What is an experimental evaluation of learning algorithms?
- How do we Evaluate predictions? and What is an absolute error? (Evaluate predictions)
- What is the sum of squares error and number of misclassification? (Evaluate predictions)
- What is the confusion matrix?
- What is accuracy, precision, and recall? (evaluate predictions)
- What is sample error and true error?
- What are the sources of errors?
- What are the difficulties in evaluating the hypothesis with limited data and possible solutions?
- How can we evaluate with limited training data?
- What is K fold cross validation trade-off in Machine Learning?

**Tutorial I**- Introduction to Tutorial I
- Types of learning: supervised vs unsupervised learning
- Example of supervised vs unsupervised learning
- Types of features: categorical vs continuous features
- Types of supervised learning: regression vs classification
- Bias vs Variance
- Generalization performance of a learning algorithm

**Linear Regression**- What is regression? (Linear functions and other functions) and What are various Types of regression models?
- What is the linear regression?
- Looking at an example of a training set for regression
- What is multiple linear regression?
- What assumption are we making for errors?
- The least squares regression line
- How do we learn the parameters (for single regression and for multiple linear regression)
- What is the delta or LMS method and how do we use gradient descent?
- What is LMS update or delta rule, batch descent and stochastic gradient descent?

**Introduction to Decision Trees**- What is a decision tree?
- How to draw a sample decision trees for discrete data?
- How to draw a sample decision trees for continuous data?
- Generate a decision tree from training examples
- Decision tree for playing tennis
- Introduction to ID3 (searching for a good tree )

**Learning Decision Tree**- How do we select attributes for the decision tree? (information gain, entropy)
- Example of creating a decision tree (using the ID3 algorithm)
- What is the GINI Index?
- How do we split continuous attributes and what are the practical issues in classification
- Practical issues in classification

**Overfitting**- What is overfitting?
- An example of underfitting and overfitting
- Overfitting due to noise or insufficient examples
- How to avoid overfitting?
- What is MDL?
- What are the conditions for pre-pruning?
- How do we use reduced error pruning for post pruning?
- What are the triple tradeoffs in model selection and generalization?
- What is regularization?

**Python exercise on decision tree and linear regression**- Python exercise on linear regression
- Python exercise on logistic regression
- Python exercise on decision tree regression

**Tutorial II**- How to solve a sample problem in linear regression?
- How to solve problems related to decision trees?
- How to find the entropy of a set and use in decision trees?
- What is the information gain?

**K-Nearest Neighbour**- What are instance-based learning and K-Nearest Neighbour algorithm?
- What are the standard distance function (Euclidean distance) and the 3 issues related to it?
- What are some examples of K-Nearest Neighbour and what is the impact of k?
- How can we use weighted distance functions?
- Why do we need to remove extra features?
- What are the various approaches to giving weights?

**Feature Selection**- Why do we need feature reduction?
- What is the curse of dimensionality?
- How can we do feature reduction? (selection and extraction)
- How can we evaluate feature subset? (wrapper / supervised and filter / unsupervised)
- How can we use the feature selection algorithm? (forward and backward selection algorithm)
- What are univariate feature selection methods?
- What are multivariate feature selection methods?

**Feature Extraction**- What is feature extraction and what kind of features do we want?
- What are the principal components (PCs) and how do we choose features?
- How do we choose the direction of the principal components (PCs) and how do we use PCA?
- How do we choose a feature (axis) for classification and how is Linear Discriminant Analysis useful?

**Collaborative Filtering**- What is a recommender system?
- How can we formally define recommendation problem?
- What are the two types of recommendation systems? (content, collaborative filtering)
- What are the two types of collaborative filtering? (used based nearest nbr, item-based nearest nbr)
- What are the two phases of algorithms for collaborative filtering? (nbr formation, recommendation)
- What are the issues with user-based KNN CF?
- What is item-based collaborative filtering?

**Python Exercise on KNN and PCA**- What will we cover?
- How do we use KNeighbors classifier in Python?
- How do we use randomized PCA in Python?
- How can we do face recognition using PCA and KNN?

**Tutorial III**- What is the curse of dimensionality?
- What is feature selection?
- What is feature reduction and PCA? (principal component analysis)
- How do you calculate the eigenvalues and eigenvector of a matrix?
- What is K-NN (K Nearest Neighbour) classification?

**Bayesian Learning**- How is probability used for modeling concepts?
- What is the Bayes theorem?
- Can we look at an example of Bayes theorem?
- How can the Bayes theorem be applied to find the hypothesis in Machine Learning? (MAP hypothesis)
- What is the Bayes optimal classifier?
- Gibbs sampling

**Naive Bayes**- Naive Bayes algorithm
- Naive Bayes algorithm for discrete x
- What is smoothing and why is it required?
- Can we look at an example of a Naive Bayes algorithm for discrete x?
- How do we use smoothing when estimating parameters?
- What is the assumption that we made in Naive Bayes and what happens if it is invalid?
- What is gaussian Naive Bayes? (for continuous X, but discrete Y)
- What are Bayesian networks?

**Bayesian Network**- Why do we need a Bayes network?
- Can we look at an example of Bayes network?
- What does a Bayesian network represent?
- What can we do with a Bayesian network (Inference)?
- Where can we apply Bayesian network?
- How do we define a Bayesian network?
- What is the graphical representation of the Naive Bayes model?
- What is the hidden Markov model?
- How is learning helped by Bayesian belief networks?

**Python Exercise on Naive Bayes**- How to use the Naive Bayes classifier?
- What is the Naive Bayes classifier?
- How is the Naive Bayes classifier relevant in the context of email spam classification?

**Tutorial IV**- How do we estimate the probabilities using the frequency distribution of probability?
- How do we use Bayes rule?
- What is MAP inference?
- What is the Naive Bayes assumption?
- What is Bayesian networks (the structures), inference and marginalization?

**Logistic Regression**- What are Logistic Regression (for Classification problems) and sigmoid function?
- What are some of the Interesting Properties of Sigmoid function?
- How can we use stochastic gradient descent with logistic regression?

**Introduction Support Vector Machine**- Support vector machine
- Functional margin
- The functional margin of a set of point
- Solving the optimization problem

**SVM The Dual Formulation**- Lagrangian duality in brief
- The KKT conditions
- Implication of Lagrangian
- The dual problem

**SVM Maximum Margin with Noise**- Linear SVM formulation
- Limitation of previous SVM formulation
- What objective to be minimized?
- Lagrangian
- Dual formulation

**Nonlinear SVM and Kernel Function**- Non-linear SVM, feature space, and kernel function
- Kernel trick
- Commonly used the kernel function
- Performance

**SVM Solution to the Dual Problem**- SMO algorithm (sequential optimization)
- Coordinate ascent
- SMO (for the dual problem)

**Python Exercise on SVM**- Support vector classification
- Visualize the decision boundaries
- Load data

**Introduction to NN**- Neural network and neuron
- Perceptron - basic unit in NN
- Gradient descent
- Stochastic gradient descent
- Multi-layer networks - by stochastic many NN

**Multilayer Neural Network**- Limitation of perceptrons
- Multi-layer NN
- Power/ Expressiveness of multilayer networks
- Two-layer back-propagation neural network
- Learning for BP nets
- Derivation

**Neural Network and Backpropagation Algorithm**- Single layer perceptron and boolean functions (OR, XOR)
- Representation capability of NNs
- Learning in multilayer N using backpropagation
- Derivation
- Backpropagation algorithm
- Training practices: batch vs stochastic and learning in the epoch
- Overfitting in anns and local minima

**Deep Neural Network**- Deep learning
- Hierarchical representation & unsupervised pre-training
- Architecture & Training
- Pooling
- CNN properties

**Python Exercise on Neural Network**- How can we create an artificial neural network using TensorFlow and TFLearn to recognize handwritten digits?
- How do we load dependencies (to recognize handwritten digits)?
- How do we load the data (to recognize handwritten digits)?
- How do we make the model (to recognize handwritten digits)?
- How do we train the model (to recognize handwritten digits)?
- What is our takeaway from this exercise (to recognize handwritten digits)?

**Tutorial VI**- What is the perception?
- What is the perceptron learning rule?
- How do we represent a boolean function using a perceptron?
- What is the forward and backward pass algorithm or backpropagation algorithm?
- Stochastic gradient descent and batch gradient descent
- A quick overview of some deep learning algorithms

**Introduction to Computational Learning Theory**- The goal of learning theory & Core aspect of Machine Learning
- PAC
- Prototypical concept learning task

**Sample Complexity Finite Hypothesis Space**- What is Sample Complexity?
- Can we look at an example of the consistent case?
- What is Find-S algorithm and what can it do?

**VC Dimension**- What kind of theorems do we have when hypothesis state is infinite?
- What is shattering?
- What is the definition of VC dimension?
- What is the upper bound and lower band on sample complexity with VC?

**Introduction to Ensembles**- What is ensemble learning?
- How can we use weak learners?
- How can we combine learners in Bayesian classifiers?
- Why are ensembles successful and what are the main challenges with them?

**Bagging and Boosting**- What is Bagging?
- What is Boosting and what is AdaBoost?
- Why does ensembling work?

**Introduction to Clustering**- What is unsupervised learning and clustering?
- What are some applications of clustering, and what are various aspects of clustering?
- Major clustering approaches
- How can we measure the quality of clustering?

**K-means Clustering**- What is the K-means algorithm?
- How can we describe the K-means Algorithm, and can we look at an illustration of it?
- What are the similarity and distance measures?
- What is the proof of convergence of K-means, time complexity, advantages, and disadvantages?
- What is model-based clustering?
- How can we apply K-means on an RGB image?
- What is EM algorithm?

**Agglomerative Hierarchical Clustering**- What is hierarchical clustering, bottom up and top down clustering?
- What is a Dendrogram?
- What is the algorithm for Agglomerative Hierarchical Clustering?
- What is the complete link method?
- What is average link clustering?

**Python Exercise on K-means clustering**- Can we look at python code for K means algorithm?
- Can we look at python code for Gaussian mixture model?
- Hierarchical agglomerative clustering

**Tutorial VIII**- What is K-means clustering?
- Solving a sample problem n K-means clustering
- What is agglomerative hierarchical clustering?
- What is Gaussian mixture model?
- Machine Learning Final Quiz

Course

4

Course 4

Learn Machine Learning through Python

**Module 1: Python Exercise on Decision Tree and Linear Regression**- Python exercise on linear regression
- Python exercise on logistic regression
- Python exercise on decision tree regression

**Module 2: Tutorial I**- How to solve a sample problem in Linear Regression?
- How to solve problems related to Decision Trees?
- How to find the entropy of a set and use in decision trees?
- What is the information gain?

**Module 3: Python Exercise on KNN and PCA**- How do we use K-Neighbors Classifier in Python?
- How do we use Randomized PCA in Python?
- How can we do Face recognition using PCA and KNN?

**Module 4: Tutorial II**- What is the curse of dimensionality?
- What is feature selection?
- What is feature reduction and PCA? (principal component analysis)
- How do you calculate the eigenvalues and Eigenvector of a matrix?
- What is K-NN (K Nearest Neighbour) Classification?

**Module 5: Python Exercise on Naive Bayes**- How to use the Naïve Bayes classifier?
- What is the Naive Bayes Classifier?
- How is the Naive Bayes Classifier relevant in the context of email spam classification?

**Module 6: Tutorial III**- How do we estimate the probabilities using the frequency distribution of probability
- How do we use Bayes rule
- What is MAP inference
- What is the Naive Bayes assumption
- What is Bayesian networks (the structures), inference and marginalization?

**Module 7: Python Exercise on SVM**- Support vector classification
- Visualize the decision boundaries
- Load data

**Module 8: Python Exercise on Neural Network**- How can we create an artificial neural network using TensorFlow and TFLearn to recognize handwritten digits?
- How do we Load dependencies (to recognize handwritten digits)?
- How do we Load the data (to recognize handwritten digits)?
- How do we make the model (to recognize handwritten digits)?
- How do we train the model (to recognize handwritten digits)?
- What is our takeaway from this exercise (to recognize handwritten digits)?

**Module 9: Tutorial IV**- What is a Perceptron?
- What is Perceptron learning rule?
- How do we represent a Boolean function using a Perceptron?
- What is the forward and backward pass algorithm or backpropagation algorithm?
- Stochastic gradient descent and Batch gradient descent
- The quick overview of some deep learning algorithms

**Module 10: Python Exercise on K-means Clustering**- Can we look at python code for K Means algorithm?
- Can we look at python code for Gaussian mixture model?
- Hierarchical Agglomerative Clustering

**Module 11: Tutorial V**- What is K-means clustering?
- Solving a sample problem n K-means clustering?
- What is Agglomerative Hierarchical clustering?
- What is the Gaussian Mixture Model?

Course

4

Course 4

Introduction to Artificial Intelligence

**Introduction**- What is AI: Some Definitions?
- Can Machines Think?
- What is Arithmetic (as related to AI)?
- What are some basic ideas in Representation and Reasoning?

**Introduction to Knowledge Representation and Reasoning**- What are some more ideas in Knowledge Representation and reasoning?
- What is the history of AI (in brief)?
- What is Physical Symbol system hypothesis?
- What are some important features of intelligent agents?

**An Introduction to Formal Logic**- What is formal logic?
- What are the various types of logic?
- What are the different Types of First Order Logic?
- What are the Properties of Logic systems?

**Propositional Logic: Language, Semantics and Reasoning**- How is knowledge represented in Propositional Logic?
- How is reasoning related to Propositional Logic?
- What is Propositional Language?
- What is the syntax of Propositional Language?

**Propositional Logic: Syntax and Truth Values**- What are the formulae related to Propositional Language?
- What are truth values as related to Propositional Logic?
- How many binary connectives do we need?
- What are the types of formulae in Propositional Logic?

**Propositional Logic: Valid Arguments and Proof Systems**- Can we look at a proof system in Propositional Logic 1/2?
- What are the Rules of inference in Propositional Logic?
- What are the Rules of Substitution in Propositional Logic?
- Can we look at a proof system in Propositional Logic 2/2?

**Propositional Logic: Rules of Inference and Natural Deduction**- Can we have a recap of Propositional Language?
- Can we have a recap of Proof systems?
- What are indirect and direct Proof systems?
- What are some examples of Proof systems?

**Propositional Logic: Axiomatic Systems and Hilbert Style Proofs**- What is Frege's system as related to Propositional Logic?
- What are derived rules in Propositional Logic?
- What are Hilbert Style Proofs in Propositional Logic?

**Propositional Logic: The Tableau Method**- What is the Tableau Method?
- What are the rules of the Tableau Method?
- What are some examples of the Tableau Method?

**Propositional Logic: The Resolution Refutation Method**- Can we have a recap of the Tableau Method?
- What is Resolution Refutation Method?
- What is the clause form of the Resolution Refutation Method?
- What is the Tautological Equivalence of the Resolution Refutation Method?
- Can we look at an example of the Resolution Refutation Method?

**Syntax**- How is First Order Logic different from Propositional Logic?
- What is the syntax of First Order Logic?
- What are the set of terms for the First Order Logic?
- What are the set of formulae for First Order Logic?
- What are the sentences in First Order Logic?

**Semantics**- What are semantics of the First Order Logic?
- What is interpretation mapping in First Order Logic?
- How is truth assignment done to formulae in First Order Logic?

**Entailment and Models**- What are Unary Relations in Semantics of First Order Logic?
- What are the restrictions of First Order Logic?
- What are the Entailment and models of First Order Logic?

**Proof Systems**- What are the Rules of inference of First Order Logic?
- What are the Rules of Substitution of First Order Logic?
- What is Modus Ponens in First Order Logic?
- What is Universal Quantifier in Implicit Quantifier Form?

**Forward Chaining**- Can we have a recap of Modified Modus Ponens?
- What is the Most General Unifier in Modified Modus Ponens?
- What is the List notation in Unification algorithm?
- How do we deal with variables, constants and lists in Unification algorithm?

**Unification**- Can we have a recap on dealing with variables, constants and lists in Unification algorithm?
- Can we look at Example 1 of Unification algorithm?
- Can we look at Example 2 of Unification algorithm?
- Can we look at Example 3 of Unification algorithm?

**Forward Chaining Rule Based Systems**- What are Reasoning algorithms in Forward chaining?
- What is Theorem Proving in Forward Chaining?
- What are Rules in Forward Chaining?
- What is Inference engine in Forward Chaining?

**The Rete Algorithm**- How is Rete Algorithm different from match algorithm?
- What is Discrimination in Rete Network?
- What is the Assimilative Part in Rete Network?
- Can we look at an example of Rule in Rete Algorithm?

**Rete Algorithm - Example**- Can we have a recap of the Rete Algorithm?
- How do we write rules in Rete network?
- How do we construct the Rete network based on rules?
- What are rule based expert systems?

**Programming in a Rule Based Language**- How are rule based expert systems used in forward chaining?
- What are some of the Patterns in Expert systems?
- What are the Negative Patterns in Expert systems?

**The OPS5 Expert System Shell**- What is OPS5?
- What are conflict resolution strategies?
- What is specificity strategy for conflict resolution?
- Can we look at an example of how conflict resolution strategies work?

**Skolemization**- What is Existential Quantifier in Representation (in FOL)?
- How is existential quantifier represented in implicit quantifier form?
- What is skolemisation?
- How do we identify the nature of a variable?

**Terminological Facts**- What is explicit and implicit form in representation in FOL-I?
- What is explicit and implicit form in representation in FOL-II?
- What is recursion in FOL?
- What are terminological facts?

**Properties and Categories**- How do we represent properties in FOL-I?
- How do we represent properties in FOL-II?
- Can we have an introduction of reification?
- How can we represent abstract ideas?

**Reification and Abstract Entities**- Can we know more about reification?
- How do we compare and add properties in FOL?
- How do we represent numbers in FOL?
- What are numbers?

**Resource Description Framework (RDF)**- What are the problems associated with using ad-hoc predicates?
- How can we use reification to present events?
- What is resource description framework?
- What are resources in RDF?

**The Event Calculus: Reasoning About Change**- What is Event Calculus?
- What are the predicates in Event Calculus?
- What are the shortcuts and axioms in Event Calculus?
- What is the Yale Shooting Problem?

**Natural Language Semantics**- What do we mean by Understanding and Expectations?
- Can we have an introduction to the CD theory?
- What are conceptualizations?
- What are state variables?

**CD Theory**- How do we make inferences using CD theory?
- What are conceptual cases?
- What are some CD actions?
- What are Instruments and state change verbs in CD theory?

**CD Theory (contd)**- How do we model various actions in CD theory-I?
- How do we model various actions in CD theory-II?
- How do we model the action "Believe" in CD theory?
- What are the physical actions that we talk about in CD theory?

**English to CD Theory**- What is conceptual analysis?
- How do you differentiate between the various senses of a word - and can we look at an example?
- What is conceptual semantics?
- What is syntactic ambiguity?

**Backward Chaining**- What is the difference between backward and forward chaining?
- Can we look at an example of backward chaining?
- How can we do programming using backward chaining?
- Can we look at an example of logic programming?

**Logic Programming**- Can we have a recap of logic programming?
- How do we define addition in logic programming?
- What are goal trees in Logic programming?

**Prolog**- Can we have a recap of addition in Logic Programming?
- What are Fibonacci sequence in Logic Programming?
- How can we make programs more efficient?
- What is Prolog?
- Can we look at an example in Prolog?

**Search in Prolog**- Can we look at some efficiency problems in Prolog?
- How can we make Prolog more efficient?
- How to make Prolog more efficient - An example I?
- How to make Prolog more efficient - An example (cont.)?

**Controlling Search**- How do we define the sort function in Logic Programming?
- How do we define the permutation function in Logic Programming?
- How do we avoid useless search in Prolog?
- What is negation by failure in Prolog?

**The Cut Operator in Prolog**- What is the Cut operator in Prolog?
- What are the different types of cut operator?
- Can we look example of different types of cut operator?
- Can we learn more about green cuts?
- Can we look at an example of green cuts?

**Incompleteness**- Can we have a recap of theorem proving?
- Can we look at a recap of how to convert formulae into clause form?
- What is incompleteness of Forward and Backward Chaining?
- What is the Completeness of Resolution Refutation Method?

**The Resolution Method for FOL**- Can we look at an example of clause form?
- What are resolution rules?
- What is the resolution refutation method for FOL?

**Clause Form**- Can we at some properties of the resolution method ?
- Can we look at an example to illustrate the semidecidability of FOL?
- How do we use answer prediction method?
- How do we use answer prediction method-An example?

**Artificial Intelligence Part -1 Final Quiz**

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