Your roadmap · आपका रोडमैप
- 1
Admission & first meetingप्रवेश और पहली मुलाकातEnquire at your nearest NDSS centre, meet the teacher, fix the batch time and get your study material.
- 2
Work planकार्य योजनाStep by step with your guide / trainer.
Exp Group 1: Python for AIExp Group 2: Uninformed SearchExp Group 3: Informed SearchExp Group 4: Game PlayingExp Group 5: Knowledge Representation and ReasoningExp Group 6: Machine Learning Basics
- 3
File, report & vivaफ़ाइल, रिपोर्ट और वाइवाPractical file / report checked, mock viva before the final one.
- 4
Online tests & resultऑनलाइन टेस्ट और रिज़ल्टTake the tests online; your marks and result show in your student login and at the centre.
सिलेबस · पाठ्यक्रम
1 subject · 6 chapters · 18 topics
01Exp Group 1: Python for AI
3 टॉपिक+
- Lists, tuples, dictionaries and functions
- NumPy arrays and pandas data frames
- Plotting with matplotlib
02Exp Group 2: Uninformed Search
3 टॉपिक+
- Water jug problem using BFS
- Missionaries and cannibals
- DFS and depth-limited search
03Exp Group 3: Informed Search
3 टॉपिक+
- 8-puzzle with A* search
- Best-first search
- Hill climbing for 8-Queens
04Exp Group 4: Game Playing
2 टॉपिक+
- Tic-tac-toe with minimax
- Alpha-beta pruning
05Exp Group 5: Knowledge Representation and Reasoning
3 टॉपिक+
- Family tree using facts and rules (Prolog / Python)
- Forward and backward chaining
- Unification of predicates
06Exp Group 6: Machine Learning Basics
4 टॉपिक+
- Linear regression
- Decision tree classifier on the Iris data set
- k-means clustering
- Accuracy and confusion matrix
About this course
आप यह कर पाएँगे
- Write Python programs using lists, dictionaries, functions and libraries such as NumPy and pandas
- Implement uninformed searches (BFS, DFS) for state-space problems
- Implement informed searches such as A* and hill climbing
- Write game-playing programs using minimax and alpha-beta pruning
- Represent knowledge with rules and facts and perform simple inference
- Train and evaluate basic machine learning models with scikit-learn
यह किसके लिए है
- MCA students of Semester 3 (AKTU, Lucknow)
- Students who want practical exposure to AI and machine learning programming
आपको क्या चाहिए
- Artificial Intelligence theory (studied alongside)
- Basic Python; lab computer with Python, Jupyter and scikit-learn (provided at the centre)
Online tests & result
Practice and unit tests online; marks and result show in your student login and at the centre.
Student login →
सवाल
Which syllabus does this Artificial Intelligence Lab course follow?
It follows the AKTU (Lucknow) MCA syllabus for Semester 3. If your own university's syllabus differs, the centre follows your university's syllabus and unit order.
What happens in the lab exam?
You perform one or two experiments given by the examiner, show the output, submit the lab record and answer viva questions.
Do I need a powerful laptop?
No. All experiments use small data sets and run on any normal laptop with Python installed.
