What are the core subjects in AI domain?

Artificial Intelligence is a vast field and with the increase in the demand for AI engineers, more and more institutes have tried to provide courses that specialize in this field. The courses of each institute differ from one another but there is some core course that is same in almost all the institutes as they provide a base for future learning. Some of the core subjects in the AI domain are as follows:


• Calculus
• Linear algebra
• Probability


  • Correlations,
  • Type I and Type II error
  • Precisionand Recall

Computer Science

• Sorting algorithms (quicksort, merge sort, insertion sort, etc.)
• Shortest path algorithms (Dijkstra’s, A*)
• Tree algorithms (pre-, in-, post-order traversal)
• Memory requirement and computational cost
• Data structures – Trees: binary search tree, heap, Queues, stacks, priority queues, Linked lists, Hash map, and Hash table
• Various search algorithms – Breadth-first search, Depth-first search, etc. Uniform search, Iterative deepening search
• Constraint satisfaction
• Propositional logic, 1st Order logic, Backward and Forward chaining, Resolution Method
• Markov decision processes (MDP)
• Programming
• Any object-oriented programming – C++/Java etc.
• Any modern programming language such as Python
• Basic data science Operations

Machine Learning – Basics

• Types of learning – supervised, unsupervised, reinforced learning
• Regression (Linear, Polynomial and Logistic regression), Classification
• Various activation functions and loss functions
• Gradient descent
• Bias, variance tradeoff
• Imp of training, test, and validation data

Deep Learning

Deep learning – A neural network with many hidden layers. Requires greater computing power.
• Pre-training, Transfer learning
• Autoencoders, Ensemble methods, Dropout, etc.
Computer Vision (Convolutional Neural Network – CNN)
CNN is used in image recognition
• Convolution operation, 2D, 3D Filters, Max pooling
• ConvNet, ResNet, GoogLeNet

Recurrent Neural Network (RNN)

RNN is used in sequential learning problems such as text/audio/video prediction
• Word embeddings, LSTM algorithm
• Backpropagation through time (BPTT)
Reinforcement Learning (RL)
RL is used in autonomous car driving, speech translation, Gaming (Famous AlphaGo program), Robotics, Algorithmic trading, etc.
• Exploration – Exploitation tradeoff, Bandit Algorithm
• Policy Gradient, Value Function
• Temporal difference learning, Q learning
• Dynamic Programming
• Function Approximation

Deep Reinforcement Learning (Deep RL)

• Neural Networks as function approximators
• Deep Q learning

Internet of Things (IoT)

• Involves learning about some hardware too)
• Different types of sensors, Actuators, Wireless protocols
• Cloud computing
• Machine to Machine (M2M) and V2V (Vehicle to Vehicle) communication
• Smart homes, Smart Grid, Smart city