Artificial Intelligence and Machine Learning syllabus : Latest Updated Syllabus for syllabus

Module-1: Fundamentals of Computer Science

Programming Languages

1. Python 

• Data Types 
• Control Flow Functions 
• Modules and Packages Object-Oriented Programming 
• File Handling
• Exception Handling 

2. C++ 

• Pointers 
• References
• Memory Management Templates 
• Standard Template Library (STL)

3. Java 

• Java Virtual Machine (JVM)
• Garbage Collection Multithreading Generics
• Collections Framework

Data Structure and Algorithms

1. Arrays

• Static Array
• Dynamic Arrays 

2. Linked Lists

• Singly Linked Lists 
• Doubly Linked Lists 
• Circular Linked Lists 

3. Stacks

• Array Implementation
• Linked List Implementation 

4. Queues

• Array Implementation
• Linked List Implementation
• Circular Queues
• Deques 

5. Trees 

• Binary Trees 
• Binary Search Trees, AVL Trees 
• Red-Black Trees, B-Trees 

6. Graphs 

• Graph Representations 
• Graph Traversals Shortest Path Algorithms 
• Minimum Spanning Tree Algorithms

7. Sorting Algorithms

• Bubble Sort 
• Selection Sort
• Insertion Sort
• Merge Sort 
• Quick Sort 
• Heap Sort 

8. Searching Algorithms 

• Linear Search 
• Binary Search

9. Dynamic Programming

• Memoization Tabulation 

10. Greedy Algorithms

• Kruskal's Algorithm 
• Prim's Algorithm
• Huffman Coding

Object-Oriented Programming (OOPs)

1. Classes and Objects

• Class Attributes 
• Class Methods
• Instance Attributes
• Instance Methods 

2. Inheritance

• Single Inheritance
• Multiple Inheritance
• Multilevel Inheritance
• Hierarchical Inheritance

3. Polymorphism

• Overloading 
• Overriding 

4. Encapsulation

• Public, Private, and Protected Access Modifiers

5. Abstraction 

• Abstract Classes
• Interfaces

Module 2: Mathematics for AI

Linear Algebra

1. Matrices

• Operations Determinants
• Inverses 

2. Vector Spaces

• Operations
• Span and Linear Independence 
• Basis and Dimension

3. Eigenvalues and Eigenvectors

• Characteristic Equation
• Diagonalization

4. Singular Value Decomposition (SVD)

• Applications of SVD

Calculus

1. Limits 

• Limit Laws 

2. Continuity 

• Derivatives 

• Rules of Differentiation 
• Partial Derivatives

3. Integrals

• Indefinite Integrals 
• Definite Integrals 
• Techniques of Integration

4. Optimization 

• Unconstrained Optimization
• Constrained Optimization

Probability & Statistics

1. Probability Distributions 

• Discrete Distributions
• Continuous Distributions

2. Hypothesis Testing 

• Null and Alternative Hypotheses 
• Types of Errors 
• Statistical Significance

3. Bayesian Statistics 

• Bayes' Theorem 
• Prior and Posterior Distributions

3. Sampling Techniques

• Simple Random Sampling 
• Stratified Sampling 
• Cluster Sampling

Module 3: Machine Learning

Supervised Learning

1. Linear Regression 

• Simple Linear Regression 
• Multiple Linear Regression 
• Polynomial Regression 
• Regularization (Ridge, Lasso, Elastic Net) 

2. Logistic Regression 

• Binary Logistic Regression 
• Multinomial Logistic Regression

3. Decision Trees 

• Information Gain and Entropy 
• Pruning 

3. Support Vector Machines 

• Linear SVM 
• Kernel Trick 

4. Ensemble Methods 

• Random Forests 
• AdaBoost
• Gradient Boosting

Unsupervised Learning

1. Clustering

• K-Means 
• Hierarchical Clustering 
• DBSCAN 

2. Dimensionality Reduction 

• Principal Component Analysis (PCA) 
• t-SNE 
• Autoencoders 

3. Association Rule Mining

• Apriori Algorithm 
• FP-Growth Algorithm

Deep Learning

1. Artificial Neural Networks (ANN)

• Perceptron
• Feedforward Neural Networks
• Backpropagation

2. Convolutional Neural Networks (CNN)

• Convolution Operations
• Pooling
• Architecture (LeNet, AlexNet, VGGNet, ResNet, etc.) 

3. Recurrent Neural Networks (RNN)

• Basic RNN
• Long Short-Term Memory (LSTM) 
• Gated Recurrent Unit (GRU)

4. Generative Adversarial Networks (GANs)

• Generator and Discriminator
• Applications of GANs

5. Reinforcement Learning

• Markov Decision Processes
• Q-Learning
• Deep Q-Networks (DQN)

Natural Language Processing (NLP)

1. Text Preprocessing

• Tokenization
• Stemming and Lemmatization
• Stop Word Removal

2. Bag-of-Words and TF-IDF

• Bag-of-Words Model
• TF-IDF Weighting

3. Word Embeddings

• Word2Vec
• GloVe
• FastText 

4. Sentiment Analysis

• Rule-based Approaches
• Machine Learning Approaches

5. Named Entity Recognition

• Sequence Labeling
• Conditional Random Fields (CRF)

6. Machine Translation

• Statistical Machine Translation
• Neural Machine Translation

Module 4: Computer Vision

Image Processing

1. Image Representation

• Grayscale and Color Images
• Digital Image Formats

2. Image Enhancement

• Brightness and Contrast Adjustment
• Spatial Filtering
• Histogram Equalization

3. Image Segmentation

• Thresholding 
• Edge Detection
• Region-based Segmentation

4. Feature Extraction

• Edge and Corner Detection
• Blob Detection 
• Scale-Invariant Feature Transform (SIFT)

Object Detection and Recognition

1. Convolutional Neural Networks (CNN) 

• Architecture (LeNet, AlexNet, VGGNet, ResNet, etc.) 

2. Region-based Convolutional Neural Networks (R-CNN)

• R-CNN
• Fast R-CNN
• Faster R-CNN

3. You Only Look Once (YOLO)

• YOLO v1 
• YOLOv2
• YOLOv3 

4. Single Shot Detector (SSD)

Module 5: Software Engineering and System Design

Software Engineering Principles

1. Agile Methodologies

• Scrum
• Kanban
• Extreme Programming (XP)

2. Version Control (Git)

• Git Basics
• Branching and Merging
• Collaborative Development
• Git Workflows

3. Testing and Debugging

• Unit Testing
• Integration Testing
• Debugging Techniques 
• Test-Driven Development (TDD)

4. Software Architecture

• Monolithic Architecture
• Microservices Architecture
• Event-Driven Architecture
• Service-Oriented Architecture (SOA)
• Serverless Architecture

Systems Design

1. Scalability

• Vertical Scaling
• Horizontal Scaling
• Load Balancing

2. Distributed Systems

• Consistency Models
• Replication Strategies
• Consensus Algorithms

3. Database Systems

• Relational Databases
• NoSQL Databases
• Data Modeling

4. Caching

• Client-side Caching
• Server-side Caching
• Cache Invalidation Strategies

5. Load Balancing

• Hardware Load Balancers
• Software Load Balancers
• Load Balancing Algorithms

Module 6: Machine Learning Models

1. Linear Regression

• Simple Linear Regression
• Multiple Linear Regression
• Polynomial Regression
• Ridge Regression (L2)
• Lasso Regression (L1)
• Elastic Net Regression
• Weighted Linear Regression
• Stepwise Regression

2. Logistic Regression

• Binary Logistic Regression
• Multinomial Logistic Regression
• Ordinal Logistic Regression
• Probit Regression
• Regularized Logistic Regression
• Weighted Logistic Regression

3. Decision Trees

• Classification Trees (CART)
• Regression Trees
• ID3 (Iterative Dichotomiser 3)
• C4.5
• CHAID (Chi-square Automatic Interaction Detection)
• Conditional Decision Trees
• Pruned Trees
• Boosted Trees

4. Random Forests

• Classification Random Forests
• Regression Random Forests
• Balanced Random Forests
• Weighted Random Forests
• Extremely Randomized Trees (Extra Trees)
• Quantile Regression Forests
• Rotation Forests

5. Support Vector Machines (SVMs)

• Linear SVM
• Non-linear SVM
• Kernel SVM
• Nu-SVM
• Weighted SVM
• Least Squares SVM
• Support Vector Regression (SVR)
• One-Class SVM

6. K-Nearest Neighbors (KNN)

• Weighted KNN
• Condensed KNN
• Modified KNN
• K-Nearest Centroid
• Local Weighted Learning
• Distance-Weighted KNN
• Kernel KNN
• Ball Tree KNN

7. Naive Bayes

• Gaussian Naive Bayes
• Multinomial Naive Bayes
• Bernoulli Naive Bayes
• Complement Naive Bayes
• Categorical Naive Bayes
• Label-Powerset Naive Bayes
• TF-IDF Naive Bayes
• Selective Naive Bayes

8. Gradient Boosting

• XGBoost
• LightGBM
• CatBoost
• AdaBoost
• Gradient Boosting Machine (GBM)
• Stochastic Gradient Boosting
• NGBoost
• Histogram-based Gradient Boosting

9. K-Means Clustering

• Lloyd's K-means
• Elkan K-means
• Mini-batch K-means
• K-means++
• Fuzzy K-means
• Kernel K-means
• Spherical K-means
• Bisecting K-means

10. Principal Component Analysis (PCA)

• Standard PCA
• Kernel PCA
• Incremental PCA
• Sparse PCA
• Probabilistic PCA
• Robust PCA
• Non-linear PCA
• Randomized PCA

11. Singular Value Decomposition (SVD)

• Full SVD
• Truncated SVD
• Randomized SVD
• Incremental SVD
• Sparse SVD
• Robust SVD
• Higher-Order SVD
• Online SVD

12. Neural Networks

• Feed-forward Neural Networks
• Convolutional Neural Networks (CNN)
• Recurrent Neural Networks (RNN)
• Long Short-Term Memory (LSTM)
• Gated Recurrent Units (GRU)
• Autoencoders
• Transformers
• Graph Neural Networks (GNN)
• Generative Adversarial Networks (GAN)
• Deep Belief Networks (DBN)
• Radial Basis Function Networks (RBFN)
• Self-Organizing Maps (SOM)

Module 7: Practical Experience and Projects

Kaggle Competitions

1. Understanding Problem Statements
2. Data Exploration and Preprocessing
3. Model Selection and Training 
4. Evaluation and Submission

Open-Source Contributions

1. Finding Relevant Projects
2. Understanding Codebase
3. Submitting Pull Requests
4. Code Reviews and Collaboration

Personal Projects

1. Image Recognition

• Object Detection
• Image Classification
• Image Segmentation

2. Natural Language Processing

• Text Classification
• Named Entity Recognition
• Machine Translation

3. Recommendation Systems

• Collaborative Filtering
• Content-based Filtering
• Hybrid Recommendation Systems

4. Reinforcement Learning Applications

• Gaming Environments
• Robotics
• Finance and Trading

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