1. Artificial Intelligence: A Modern Approach by Stuart J. Russell and Peter Norvig (2009): This textbook is widely regarded as the most comprehensive exploration of AI available, covering all facets from search algorithms, game playing machines, robotics to natural language processing. It also contains a wealth of code examples for readers interested in learning how to program their own AI applications.
2. The Elements of AI by Reaktor and the University of Helsinki (2018): This book offers an accessible entry point into studying artificial intelligence, machine learning and deep learning practices and provides an overview on various concepts such as convolutional networks, recurrent neural networks, supervised & unsupervised learning among others – without requiring any previous programming experience.
3. Reinforcement Learning: An Introduction by Richard S. Sutton and Andrew G. Barto (2018): Written by two leading researchers in the field of reinforcement learning this volume presents a unified treatment of machine learning methods drawn from neural networks, control theory, evolutionary computation and other areas – essentially making it one of the best books on reinforcement training available today!
4. Machine Learning Yearning-Technical Strategy for AI Engineers in the Era Of Deep Learning By Andrew Ng (2018): As part of his series on effective machine learning strategies this book looks at scenarios where data sets are limited or non-existent; which can occur when working with sensitive data like healthcare patient info; how to prioritize development efforts for maximum impact; ways for speedier outcomes through using transfer-learning techniques among other topics related specifically to applied deep learning engineering practises .

Heuristic search techniques are an important area of Artificial Intelligence (AI) research. Heuristic search is a form of problem-solving where the goal is to find a solution path which leads to the goal state on a problem or task, using heuristics. In AI, the term ‘heuristics’ refers to rules of thumb or strategies which are used to reduce the number of possible solutions in a given problem space. Heuristic search algorithms use these heuristics as a way of guiding their progress through the search space towards finding an optiomal solution. Some common heuristic search techniques include Depth-First Search (DFS), Breadth-First Search (BFS), A* Search, Hill Climbing and Simulated Annealing. Each technique has its own advantages and disadvantages but all share one purpose – to efficiently explore potential paths towards solving a problem.
Depth-first Search (DFS) is an example of recursive backtracking: starting at one point in the graph and iteratively exploring every avenue available until you reach your destination or hit dead ends, then backtracking and trying another approach with successive branches from other points in the graph. This can be useful for quickly locating solutions in deep or large graphs, although it tends to get stuck in loops without any mechanism for finding better options on parts that have already been explored – DFS does not take into consideration previously found paths when looking up new ones so it won’t detect cycles easily if at all by itself.

Breadth-first Search (BFS) is similar but works differently than DFS; instead of going deeper into one branch before exploring other possibilities from different starting points like DFS does, BFS looks initially at all those possible options first before delving further down specific branches – this can make it more effective for certain types problems where shorter overall paths are desired over deep searches with many backtracks along multiple levels deep within trees/graphs structures.

Dabbala Rajagopal “Raj” Reddy (born June 13, 1937) is an Indian-born American computer scientist and Turing Award winner. He was an early artificial intelligence pioneer and has been on the faculties of Stanford and Carnegie Mellon for over 50 years. [4] He was the founding director of Carnegie Mellon University’s Robotics Institute. He was instrumental in establishing India’s Rajiv Gandhi University of Knowledge Technologies to meet the educational needs of low-income, gifted rural youth. He is the president and CEO of the International Institute of Information Technology in Hyderabad. He was the first person of Asian origin to receive the Turing Award, also known as the Nobel Prize in Computer Science, in 1994 for his work in the field of artificial intelligence.

Completing a BTech in Artificial Intelligence can open up a range of job opportunities in various industries. Here are some potential job prospects for graduates:

1. AI Engineer/Developer: As an AI engineer/developer, you will be responsible for designing, developing, and implementing AI systems and algorithms for various applications, such as natural language processing, computer vision, and predictive analytics. You may work in industries such as finance, healthcare, or manufacturing.
2. Machine Learning Engineer: As a machine learning engineer, you will be responsible for building and deploying machine learning models for various applications, such as fraud detection, recommendation engines, and predictive maintenance. You may work in industries such as e-commerce, gaming, or advertising.
3. Data Scientist: As a data scientist, you will be responsible for analyzing large datasets to extract insights and identify patterns. You may work in industries such as finance, marketing, or healthcare, and will need to be proficient in programming languages such as Python, R, and SQL.
4. Robotics Engineer: As a robotics engineer, you will be responsible for designing and developing robots and other autonomous systems. You may work in industries such as manufacturing, healthcare, or agriculture.
5. AI Product Manager: As an AI product manager, you will be responsible for managing the development and deployment of AI-powered products and services. You will need to have a strong understanding of both AI technology and product management.
6. Research Scientist: As a research scientist, you will be responsible for conducting research and developing new AI technologies and algorithms. You may work in academia, government, or industry research labs.

Overall, the job prospects after completing a BTech in Artificial Intelligence are promising, as AI continues to transform various industries and becomes more integrated into our daily lives. However, as with any field, competition for jobs can be fierce, and it’s important for graduates to have a strong foundation in both AI theory and practical skills.

The field of artificial intelligence owes much to the work of John McCarthy, who is widely considered as the Father of AI. McCarthy was an American computer scientist and cognitive scientist who was born in 1927 and passed away in 2011. He coined the term “artificial intelligence” in the summer of 1956, and it became a catalyst for a new field of study.

McCarthy made significant contributions to various areas of computer science and AI, including the development of LISP programming language, which is still used in AI research today. He also co-founded the MIT Artificial Intelligence Laboratory (now the MIT Computer Science and Artificial Intelligence Laboratory) and was instrumental in creating the first AI conference in 1956.

Moreover, McCarthy’s research focused on formalizing the processes of human reasoning and problem-solving. He proposed a number of key concepts in AI, including the idea of using logic to represent and manipulate knowledge, and the development of algorithms for reasoning and problem-solving. He also made significant contributions to the field of robotics, including the development of the first AI-controlled robotic arm.

Overall, McCarthy’s work laid the foundation for the field of AI as we know it today, and his legacy continues to inspire and inform researchers in the field.

John McCarthy is considered the father of artificial intelligence (AI). He coined the term “artificial intelligence” in 1956 and was one of the pioneers of the field. McCarthy was a mathematician and computer scientist who played a significant role in the development of AI research.

McCarthy is best known for his contributions to the field of logic-based AI, particularly in the development of the LISP programming language. LISP is still widely used in AI programming today and is considered one of the foundational languages of the field.

McCarthy was also instrumental in organizing the Dartmouth Conference in 1956, which is widely regarded as the birthplace of AI. The conference brought together many of the leading researchers in the field and set the agenda for AI research for decades to come.

McCarthy’s contributions to the field of AI were numerous, and he was recognized for his work with numerous awards and honors throughout his career. He was awarded the Turing Award in 1971, which is considered the highest honor in computer science.

While McCarthy is often credited with being the father of AI, it is important to note that many other researchers made significant contributions to the field over the years. Nonetheless, McCarthy’s legacy lives on, and his work continues to influence AI research and development today.

Like every situation has got some positives and negatives, Similarly Artificial Intelligence has also got it positive side and artificial intelligence disadvantages are also evident.
Following are some of the artificial intelligence disadvantages.
1. Unemployment
Definitely one of the major artificial intelligence disadvantages. Artificial Intelligence uses robot, which is displacing occupations and increasing unemployment (in a few cases). Therefore, people claim that there is always a chance of unemployment as a result of robots replacing humans.
For instance, robots are frequently used to replace human power in manufacturing businesses in some more technologically advanced nations .
2. Highly expensive
The power to create a machine that can act similar to human intelligence is no small task. It requires lots of time and resources and can cost a huge amount of money. AI also needs to function on the latest software and hardware to stay updated and meet the latest expectations, thus making it quite costly.
3. Lack of Ethics
Ethics and morality are important features of a human being that can be difficult to incorporate into an AI machine. The fast progress of AI has raised a number of concerns that some day, AI will grow uncontrollably, and eventually wipe out humans. This phase is referred to as the AI singularity. Thus, it acts as one of the prominent artificial intelligence disadvantages.
4.Increase in Laziness
As we do not have to remember things or solve puzzles to get the job done, we tend to use our brains very less. This addiction to AI can cause major problems to upcoming generations.
5.Lack of improvement
Humans cannot develop A.I because it is a technology based on pre-programmed facts and experience. AI is effective at repeatedly carrying out the same task, but if we want any improvements or adjustments we must manually alter the codes. AI cannot be accessed and utilized similar to human intelligence, but it can store huge amount of data. Machines can only complete tasks they have been designed for programmed for and if they are asked to complete anything else, they fail or provide useless results, which can have significant negative effects.
These are some of the artificial intelligence disadvantages which can cause havoc for human beings.

Anyone who wants to learn AI is a beginner at some time, whether it is a fresh college graduate or an individual already working and wants to move into this career path. Everyone starts somewhere someday. So, never take the pressure of the syllabus. For sure, learning is huge, but it is not complex. If followed properly, anyone can achieve it and interest matters more than the discussion on difficulty level. AI is very vast and similarly artificial intelligence syllabus is also very vast but there are some of the most important topics in artificial intelligence syllabus which are mostly relevant in the current scenario.
In this article, I will tell you about the most important artificial intelligence syllabus
Natural Language Processing : This entails understanding and generating content in natural language (e.g. English). Chatbots are an application of NLP
Machine learning: Machine learning deals with statistical and mathematical models used by algorithms to learn some functions by way of examples. ML is heavily used in all types of tech processes.I love this part of the artificial intelligence syllabus. Machine learning is a set of tools data scientists use to discover correlations between input data and desired outputs. A lot of data science projects use machine learning, but not all data science is about machine learning. Data science also involves analytic problem solving, business insight, data manipulation and other skills. The amount of time a data scientist often spends on the machine learning part is usually much lower than the time spent on analyzing the business case, cleaning up data, reshaping data into a form you can use and making sure the results make sense (cross validating, hypothesis testing etc.)
Symbolic logic: This is where A.I started or you can say the basis of all the artificial intelligence syllabus. Symbolic logic is not a field with great many applications but it is important for its historic context.
Deep learning: A related field is deep learning, where the learning takes place through black-box algorithms with several complicated/inter-connected layers of processing units. Artificial neural networks are an example of this.
These are some of the important parts of the artificial intelligence syllabus.