The machine learning syllabus mainly includes artificial intelligence which allows software applications to provide accurate results such as predicting outcomes despite not being specifically programmed for it; with the help of historical data. Computer science, artificial intelligence, data science, deep learning & statistics form the backbone of any machine learning course syllabus.
Machine Learning is one of the in-demand technologies in today's world. In any machine learning course syllabus; introduction to machine learning, machine learning algorithms, neural networks, natural language processing, regression, and programming are the core machine learning subjects.
Programming languages including Python, Java, C++, and R are also a part of the machine learning syllabus. Apart from the core subjects; hands-on project works, internships, etc are also included in the curriculum to provide maximum learning outcomes.
Table of Contents
2.2 Deep Learning
2.3 Data Science
5.1 Machine Learning Syllabus in GTU
5.2 Machine Learning Syllabus in VTU
5.3 Machine Learning Syllabus in IIT
What is Machine Learning?
- Machine learning is related to Artificial Intelligence and its applications. Machine learning enables a system to learn and improve any software applications and to make them more accurate at predicting outcomes.
- Machine learning uses data or graphs as inputs to understand entities, domains, and the connections between them.
- Introduction to machine learning, supervised learning and linear regression, classification and logistic regression, decision tree, and random forest are machine learning subjects that anyone who is willing to learn machine learning will come across.
- For a complete beginner who wants to understand what machine learning is; subjects like statistics, python, data science, deep learning, and artificial intelligence are important.
Quick Links:
Machine Learning Subjects
A complete beginner who does not have any knowledge of machine learning, data science or AI can check out the basic machine learning subjects.
| Statistics | Linear Algebra |
| Calculus | Probability |
| Programming Languages (Python and R) | Artificial Intelligence and Machine Learning |
| Natural Language Processing | Deep Learning |
| Graphical Models | Reinforcement Learning |
| Supervised and Unsupervised Learning | - |
AI and ML Syllabus
- AI and ML are closely related, in fact, while AI is a broader concept of creating machines capable of human thinking, ML is a subset of AI which deals with developing machines and making them learn using data without programming explicitly.
- They make it easier in computer science and data processing that can quickly transform a vast array of industries helping in increasing customer satisfaction, differentiated digital services, optimizing existing business services, etc.
- Introduction to Data Science and AI & ML, R Programming Essentials, Descriptive Statistics, Statistical Analysis, Data Analysis, Probability, etc are the main areas covered in any AI and ML syllabus. The table below gives the detailed AI and ML Syllabus.
| Introduction to Data Science, AI and ML | R Programming Essentials |
| Descriptive Statistics | Statistical Analysis |
| Data Analysis | Probability |
| Data Acquisition | Data Pre-processing and Preparation |
| Data Quality and Transformation | Handling Text Data |
| Principles of Big Data | Big Data Frameworks |
| Sampling and Estimation | Inferential Statistics |
| Linear Regression, Multiple Linear Regression | Non-Linear Regression |
| Forecasting models | Foundations for ML |
| Clustering | Foundations for AI |
| Convolution Neural Networks | Recurrent Neural Networks |
Deep Learning Syllabus
- Deep learning is an important part of machine learning and teaches computers to imitate the ways in which humans gain certain types of knowledge.
- Deep learning is interrelated to statistics, predictive modeling, and data science and plays an important role in machine learning.
- They can be used to produce more accurate results, sometimes better than humans, using text, images, and sound.
- Among the topics for deep learning; neural networks, convolutional neural networks, recurrent neural networks, etc are some of them.
The detailed syllabus for deep learning is mentioned below.
| Syllabus | Topics |
|---|---|
| Neural Networks | Introduction to Neural Networks, Implementing Gradient Descent, Training Neural Networks, Sentiment Analysis, Deep Learning With Pytorch |
| Convolutional Neural Networks | Cloud Computing, Convolutional Neural Network, CNNs in PyTorch, Weight Initialization, Autoencoders, Transfer Learning in PyTorch, Deep Learning for Cancer Detection |
| Recurrent Neural Networks | Recurrent Neural Networks, Long Short-Term Memory Network, Implementation of RNN & LSTM, Hyperparameters, Embeddings & Word2vec, Sentiment Prediction RNN |
| Generative Adversarial Networks | Generative Adversarial Network, GANs, PIX2PIX & Cyclegan |
| Updating a Model | Introduction to Deployment, Deploy a Model, Custom Models and Web Hosting, Model Monitoring, Updating a Model |
Machine Learning Syllabus for Data Science
- Data Science is the most important machine learning subject as computer systems learn with the help of data.
- ML algorithms depend on data delivered by data scientists and don't work without data science as they train on datasets.
- Machine learning for data science syllabus encompasses areas of study such as linear algebra, statistics, probability, programming for data analysis, etc.
- The complete list of data science topics included in the machine learning syllabus can be found here.
Read More: How is Data Science Important for Machine Learning?
| Linear Algebra | Probability and Statistics for Data Science |
| Statistical Learning | Programming for Data Analysis |
| Convex Optimization and Applications | Deep Learning |
| Scalable Learning | Software for Data Science |
| Security of Hardware Embedded System | Big Network Data |
| Computer Algorithms | - |
BTech AI and ML Syllabus
BTech AI and ML is a 4 years undergraduate course that is offered under the Engineering stream, covering various computer languages such as Prolog, Lisp, Java, and Python. The semester-wise detailed BTech AI and ML Syllabus is given in the table below.
| Semester I | Semester II |
|---|---|
| Mathematics I | Mathematics II |
| Physics | Basic Electronics Engineering |
| Physics Lab | Basic Electronics Engineering Lab |
| Programming in C Language | Data Structures with C |
| Programming in C Language Lab | Data Structures-Lab |
| Playing with Big Data | Discrete Mathematical Structures |
| Open Source and Open Standards | Introduction to IT and Cloud Infrastructure Landscape |
| Communication WKSP 1.1 | Communication WKSP 1.2 |
| Communication WKSP 1.1 Lab | Communication WKSP 1.2 Lab |
| Seminal Events in Global History | Environmental Studies |
| - | Appreciating Art Fundamentals |
| Semester III | Semester IV |
| Computer System Architecture | Operating Systems |
| Design and Analysis of Algorithms | Data Communication and Computer Networks |
| Design and Analysis of Algorithms Lab | Data Communication and Computer Networks Lab |
| Web Technologies | Introduction to Java and OOPS |
| Web Technologies Lab | Introduction to Java and OOPS Labs |
| Functional Programming in Python | Applied Statistical Analysis (for AI and ML) |
| Introduction to Internet of Things | Current Topics in AI and ML |
| Communication WKSP 2.0 | Database Management Systems & Data Modelling |
| Communication WKSP 2.0 Lab | Database Management Systems & Data Modelling Lab |
| Securing Digital Assets | Impact of Media on Society |
| Introduction to Applied Psychology | - |
| Semester V | Semester VI |
| Formal Languages & Automata Theory | Reasoning, Problem Solving and Robotics |
| Mobile Application Development | Introduction to Machine Learning |
| Algorithms for Intelligent Systems | Natural Language Processing |
| Current Topics in AI and ML | Minor Subject 2 - General Management |
| Software Engineering & Product Management | Minor Subject 3 - Modern Professional Finance |
| Minor Subject: - 1. Aspects of Modern English Literature/ Introduction to Linguistics | Design Thinking |
| Minor Project I | Communication WKSP 3.0 |
| - | Minor Project II |
| Semester VII | Semester VIII |
| Program elective | Major Projects 2 |
| Web Technologies | Program Elective-5 |
| Major Project- 1 | Program Elective-6 |
| Comprehensive Examination | Open Elective - 4 |
| Professional Ethics and Values | Universal Human Value & Ethics |
| Industrial Internship | Robotics and Intelligent Systems |
| Open Elective - 3 | - |
| CTS-5 Campus to corporate | - |
| Introduction to Deep Learning | - |
MTech AI and ML Syllabus
MTech in AI and ML is a 2 years long postgraduate course, which emphasizes on teaching science and engineering related to computer machines and making them able to perform tasks that require human intelligence. The detailed semester-wise MTech AI and ML syllabus is given in the table below.
| Semester I | Semester II |
|---|---|
| Graph Theory | Robot Programming |
| Electronics System Design | Electrical Actuators and Drives |
| Introduction to Robotics | Image Processing & Machine Vision |
| Machine and Mechanics | Robotics Based Industrial Automation |
| Embedded Systems | Robotics Control System |
| Manufacturing System Simulation | Principles of Computer Integrated Manufacturing |
| Semester III | Semester IV |
| Artificial Intelligence and Neural Network | Comp Numerical Control Machines & Adaptive Control |
| System modeling and identification | Manufacturing Systems Automation |
| Nano Robotics | Robot Economics |
| Robot Vision | Modern Material Handling Systems |
| Robotic Simulation | Group Technology and Cellular Manufacturing |
| PLC and Data Acquisition system | - |
| Summer Internship | - |
Syllabus of Machine Learning in Top Colleges
The syllabus for Machine Learning may vary from one University/ College to another depending on various factors such as course type, duration, level and providing institution. Among the top colleges for Machine Learning, GTU, VTU, IITs, Anna University, Pune University, etc are some of them. The sections below will discuss the machine learning course syllabus in all of these colleges.
Know More: Top Machine Learning Colleges
Machine Learning GTU Syllabus
GTU is set to introduce AI and Machine Learning courses starting from the academic session 2023-24 under the Professional Elective Course. The detailed syllabus for the AI and Machine Learning Course by GTU is given below in the table.
| Syllabus | Topics Covered |
|---|---|
| Introduction | Scope, Introduction to AI, History of AI, Related fields |
| Introduction to Artificial Neural Networks | Biological Neurons; Neural Networks, Artificial Neural Networks, Activation Functions, Training Methods, Supervised and Unsupervised Learning |
| Introduction to Machine Learning | Different types of Learning, Hypothesis Space, Inductive Bias, Evaluation, Cross-Validation |
| Main Algorithms used in Machine Learning | Linear Regression, Decision Trees, K-nearest Neighbour, Collaborative Filtering, Dimensionality Reduction Technique |
| Logistic Regression and Support Vector Machine | Logistic Regression, Support Vector Machine, Maximum Margin with Noise, Nonlinear SVM and Kernel Function, SVM |
| Advanced Learning methods and Clustering | Intro to Clustering, K- means Clustering, Agglomerative Hierarchical Clustering, Semi-Supervised, Reinforcement Learning, Deep Learning |
| Fuzzy Logic | Conventional set vs fuzzy set, Operations, Fuzzy rules and inference, Defuzzification, Application for control |
| Genetic Algorithm | Introduction, Steps for GA, reproduction, Crossover, Mutation, Termination parameter, Application |
Artificial Intelligence and Machine Learning VTU Syllabus
Artificial Intelligence and Machine Learning is offered in VTU as a specialized elective course under BE CSE program. The details of the artificial intelligence and machine learning syllabus under VTU is mentioned below.
| Syllabus | Topics |
|---|---|
| What is Artificial Intelligence? | Introduction, Problems; spaces & search, Heuristic search techniques |
| Knowledge Representation Issues | Knowledge Representation Issues, Predicate Logic, Representing knowledge using Rules, Concept Learning, Find-S algorithm |
| Symbolic Reasoning under Uncertainty | Symbolic Reasoning, Statistical reasoning, Weak Slot, Filler Structures |
| Bayesian Learning | Introduction, Bayes theorem and concept learning, ML and LS error hypothesis, ML for predicting, MDL principle, BBN, EM Algorithm |
| Instance-Base Learning | Introduction, k-Nearest Neighbour Learning, Locally weighted regression, Reinforcement Learning |
Machine Learning Syllabus IIT
IIT offers machine learning specialization courses under the department of computer science and engineering (CSE). It covers basic math, regression, classification, etc. Here is the detailed machine learning syllabus in IITs. Also Check: IIT Madras Data Science Courses
| Syllabus | Topics |
|---|---|
| Basic Maths | Probability, Linear Algebra, Convex Optimization |
| Background | Statistical Decision Theory, Bayesian Learning |
| Regression | Linear Regression, Ridge Regression and Lasso |
| Dimensionality Reduction | Principal Component Analysis, Partial Least Squares |
| Classification | Linear Classification, Logistic Regression, Linear Discriminant Analysis, Perceptron, Support Vector Machines + Kernels, Artificial Neural Networks + |
| Evaluation measures | Hypothesis testing, Clustering, K-means, K-medoids, Density-based Hierarchical, Spectral |
| Miscellaneous topics | Expectation-Maximization, GMMs, Intro to Reinforcement Learning |
| Graphical Models | Bayesian Networks |
Artificial Intelligence and Machine Learning Syllabus Anna University
Machine learning is taught under BTech AI and Data Science and BE/ BTech CSE programs in Anna University. The detailed syllabus for both of these is given below in the following tables.
BTech Artificial Intelligence and Data Science Syllabus
| Semester I | Semester II |
|---|---|
| Communicative English | Technical English |
| Engineering Mathematics – I | Linear Algebra |
| Engineering Physics | Data Structures Design |
| Engineering Chemistry | Environmental Science and Engineering |
| Problem Solving and Python Programming | Basic Electrical, Electronics and Measurement, Engineering |
| Engineering Graphics | Digital Principles and Computer Organization |
| Semester III | Semester IV |
| Discrete Mathematics | Probability and Statistics |
| Introduction to Operating Systems | Database Design and Management |
| Fundamentals of Data Science | Artificial Intelligence |
| Object Oriented Programming | Data Analytics |
| Design and Analysis of Algorithms | Professional Elective I |
| Semester V | Semester VI |
| Optimization Techniques | Artificial Intelligence |
| Computer Networks | Data and Information Security |
| Data Exploration and Visualization | Web Technology |
| Business Analytics | Professional Elective II |
| Machine Learning | Professional Elective III |
| Open Elective I | - |
| Semester VII | Semester VIII |
| Deep Learning | Professional Elective IV |
| Text Analytics | Professional Elective V |
| Basics of Computer Vision | - |
| Big Data Management | - |
| AI and Robotics | - |
| Open Elective II | - |
BE CSE Syllabus
| Semester I | Semester II |
|---|---|
| Foundational English | Technical English |
| Mathematics I | Mathematics II |
| Engineering Physics | Environmental Science and Engineering |
| Engineering Chemistry | Engineering Graphics |
| Computing Techniques | Electronic Devices and Circuits for Computer Engineers |
| Basic Sciences Laboratory | Programming and Data Structures I |
| Computer Practices Laboratory | Engineering Practices Laboratory |
| - | Programming and Data Structures Laboratory I |
| Semester III | Semester IV |
| Object Oriented Programming | Probability and Queuing Theory |
| Algebra and Number Theory | Design and Analysis of Algorithms |
| Digital Principles and Design | Database Management Systems |
| Electrical Engineering and Control Systems | Computer Architecture |
| Programming and Data Structures II | Operating Systems |
| Software Engineering | Principles of Management |
| Digital Laboratory | Database Management Systems Laboratory |
| Programming and Data Structures Laboratory II | Operating Systems Laboratory |
| Semester V | Semester VI |
| Data Communication and Computer Networks | Compiler Design |
| Object Oriented Analysis and Design | Machine Learning Techniques |
| Object Oriented Analysis and Design | Parallel and Distributed Computing |
| Theory of Computation | Web Programming |
| Digital Signal Processing | Professional Elective-II |
| Professional Elective-I | Professional Elective-III |
| Semester VII | Semester VIII |
| Cloud Computing Techniques | Professional Elective VI |
| Security in Computing | Open Elective II |
| Wireless Networks | - |
| Professional Elective-IV | - |
| Professional Elective-V | - |
| Open Elective-I | - |
Machine Learning Syllabus Pune University
Pune University offers Honors in Artificial Intelligence and Machine Learning in the 3rd year of engineering i.e. 5th semester, and also specialized SE (AI & ML) and BE Artificial Intelligence and Data Science in the 2nd year of engineering. Check out the complete machine learning syllabus at Pune University.
Honors in AI & ML Syllabus
| Computational Statistics | Computational Programming Laboratory |
| Artificial Intelligence | Machine Learning |
| Machine Learning Laboratory | Soft Computing and Deep Learning |
| Seminar | - |
SE (AI & ML) Syllabus
AI and ML are covered in the 2 years (4th semester) of SE in Savitribai Phule Pune University as fundamentals of artificial intelligence and machine learning. Here is the detailed syllabus for AI and ML.
| Syllabus | Topics |
|---|---|
| Introduction to AI | Basic Definition, Terminology, Intelligent Agent |
| Problem Solving | Search Algorithms in Artificial Intelligence, Techniques, Constraint Satisfaction problem |
| Knowledge and Reasoning | Knowledge-Based Agent and reasoning in Artificial intelligence |
| Introduction to ML | Introduction to Machine Learning, history, Learning types |
| Learning | Types of Learning, Supervised, Unsupervised |
| Classification & Regression | Classification, Binary, Multiclass, Regression |
BE Artificial Intelligence and Data Science
| Semester III | Semester IV |
|---|---|
| Discrete Mathematics | Statistics |
| Data Structures & Algorithms | Internet of Things |
| Object Oriented Programming (OOP) | Software Engineering |
| Computer Graphics & Computer Vision | Database Management System |
| Operating Systems | Management Information System |
| Semester V | Semester VI |
| Data Science | Data Warehousing & Mining |
| Computer Networks | Web Technology |
| Artificial Intelligence | Neural Networks & Fuzzy Logic |
| Cyber security | Elective II |
| Elective I | - |
| Semester VII | Semester VIII |
| Machine Learning | Computational Intelligence |
| Data Modeling & Visualization | Distributed Computing |
| Elective III | Elective V |
| Elective IV | Elective VI |
Machine Learning Syllabus: FAQs
Ques. What is the syllabus of machine learning?
Ans. The syllabus for Machine Learning is vast and covers various areas of study in Artificial Intelligence, Data Science, Deep Learning, etc. Here are some of the important subjects in Machine Learning:
- Programming Languages
- Machine Learning Techniques and Algorithms
- Artificial Neural Networks; applications
- Machine Learning and Artificial Intelligence
- Natural Language Processing
- Deep Learning and Reinforcement Learning.
Ques. What is the syllabus for AI and machine learning?
Ans. AI and Machine Learning Courses are offered as specializations and as courses at different levels. Among the topics for AI and Machine Learning, the important ones include
- Calculus
- Programming
- Data Structures
- Object-Oriented Programming
- Mathematical Foundations for Computing
- Algorithms
- Operating Systems
- Database Management Systems
- Machine Learning
- Advanced AI and ML.
Ques. Is machine learning hard?
Ans. Machine Learning is not very hard for beginners, however advanced areas in Machine Learning including advanced tools may be a bit hard and requires advanced mathematics, statistics, and software engineering. For beginners, it is somewhat complementary to the level of difficulty in learning programming libraries. To make it easier for beginners to learn Machine Learning, focus on using data and algorithms rather than designing one.
Ques. Is Python necessary for machine learning?
Ans. Yes, Python is necessary for Machine Learning. Python has various applications in Machine Learning such as data, models, validations, optimizing hyper-parameters, visualizing algorithms, variables, and more. If you want to learn how to use machine learning, you must first start with the basics of Python.
Ques. How do I start with machine learning?
Ans. Here are some tips to start with Machine Learning;
- Develop your mindset with positivity.
- Lay down a systematic approach to solving problems.
- Pich a tool that is suitable for you.
- Start by practicing on Datasets.
- Build a portfolio to display your experience and work.
Ques. What skills are required for machine learning?
Ans. Some of the skills required for Machine Learning are listed below;
- Applied Mathematics
- Fundamentals of Computer Science and Programming
- Machine Learning Algorithms
- Data Modeling and Evaluation
- Neural Networks
- Natural Language Processing
- Communication Skills
Ques. What is the difference between AI and machine learning?
Ans. Artificial Intelligence enables a machine to simulate human behavior while Machine learning is a branch of AI that develops and helps machines to learn automatically using previous data without programming explicitly. In short, all machine learning is AI, however, all AI may not be machine learning.
Ques. How many subjects are there in machine learning?
Ans. Machine Learning covers numerous areas from Computer Science, Artificial Intelligence, Data Science, Deep Learning, Statistics, Probability, and more. Some of the basic subjects include the Data Science Tool kit, Statistics & Exploratory Data Analytics, Machine Learning, Natural Language Processing, Deep Learning, Reinforcement Learning, etc.
Ques. What projects can I do with machine learning?
Ans. With Machine Learning, one can do various projects. Here is a list of top projects that can be done with Machine Learning.
- Recommending movies using Movielens Dataset
- TensorFlow
- Walmart Sales Forecasting
- Predicting Stock Price
- Human Activity Recognition using Smartphones
- Predicting Wine Quality
- Predicting Breast Cancer
- Iris Classification
- Sorting out Specific Tweets on Twitter
- Converting Handwritten Documents to Digitized Versions.
Ques. Can I learn machine learning without coding?
Ans. Yes, you can learn machine learning without coding. However, if you want to be impactful and secure your career in Machine Learning, a little knowledge of coding is required.
Comments